# pos

id: cord-125817-5o12mbut
pos: NNP
token:
  https://github.com/narcisoyu/Institional-and-news-media-tweet-dataset-for-COVID-19social-science-research

lemma:
  https://github.com/narcisoyu/Institional-and-news-media-tweet-dataset-for-COVID-19social-science-research


id: cord-180457-047iqerh
pos: NNPS
token:
  https://www.theguardian.com/world/2020/may/07/black-people-four-times-morelikely-to-die-from-covid-19-ons-finds

lemma:
  https://www.theguardian.com/world/2020/may/07/black-people-four-times-morelikely-to-die-from-covid-19-ons-finds


id: cord-180457-047iqerh
pos: NNP
token:
  https://thefederalist.com/2020/03/26/the-scientist-whose-doomsday-pandemicmodel-predicted-armageddon-just-walked-back-the-apocalyptic-predictions/

lemma:
  https://thefederalist.com/2020/03/26/the-scientist-whose-doomsday-pandemicmodel-predicted-armageddon-just-walked-back-the-apocalyptic-predictions/


id: cord-169484-mjtlhh5e
pos: NNP
token:
  https://www.derstandard.at/jetzt/livebericht/2000088169126/buwog-prozessvermoegensverwalter-stinksauer-auf-meischberger

lemma:
  https://www.derstandard.at/jetzt/livebericht/2000088169126/buwog-prozessvermoegensverwalter-stinksauer-auf-meischberger


id: cord-208179-9pwjnrgl
pos: NNP
token:
  https://www.forbes.com/sites/tarahaelle/2020/06/19/risking-their-lives-to-save-theirlives-why-public-health-experts-support-black-lives-matter-protests/#11b5ac96851b

lemma:
  https://www.forbes.com/sites/tarahaelle/2020/06/19/risking-their-lives-to-save-theirlives-why-public-health-experts-support-black-lives-matter-protests/#11b5ac96851b


id: cord-208179-9pwjnrgl
pos: NN
token:
  https://www.telegraph.co.uk/politics/2020/04/26/lib-dem-councillor-apologisestweeting-photo-bacon-solidarity/

lemma:
  https://www.telegraph.co.uk/politics/2020/04/26/lib-dem-councillor-apologisestweeting-photo-bacon-solidarity/


# ent

id: cord-018619-aknktp6d
type: GGP
entity:
  VIII DOI 10 1007 978 3 319 10422 5_36 sha b89f9617cf72a2d0ef1b63faf4254877ed836e75 doc_id 18619 cord_uid aknktp6d


id: cord-027431-6twmcitu
type: WORK_OF_ART
entity:
  Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms date 2020


id: cord-252344-5a0sriq9
type: DISEASE
entity:
  influenza 6 social distancing or physical distancing refers to increasing the space between individuals and avoidance of larger gatherings in an attempt to reduce viral transmission 7 This community NPI


id: cord-278119-8k2j3kjv
type: WORK_OF_ART
entity:
  The Effect of Spinal Adjustment Manipulation on Immunity and the Immune System A Rapid Review of Relevant Literature RCC Research Bulletins


id: cord-235946-6vu34vce
type: ORG
entity:
  the Office of Naval Research ONR Multidisciplinary University Research Initiative Award N000140811186 Award N000141812108


id: cord-102236-z0408dje
type: WORK_OF_ART
entity:
  Privacy Cannot Be a Casualty of the Coronavirus Evaluation of the Effectiveness of Surveillance and Containment Measures


id: cord-269093-x6taxwkx
type: ORG
entity:
  thailand International Symposium on Emerging Trends and Technologies in Libraries and Information Services Human


id: cord-269093-x6taxwkx
type: ORG
entity:
  Social Network Facebook Statuses Mining International Conference on Sciences of Electronics Technologies of Information and Telecommunications SETIT


id: cord-329999-flzqm3wh
type: ORG
entity:
  Human and Computer Personality Prediction From Digital Footprints Current Directions in Psychological Science Psychological


id: cord-329999-flzqm3wh
type: WORK_OF_ART
entity:
  How Do Personality Traits Shape Information Sharing Behaviour in Social Media Exploring the Mediating Effect of Generalized Trust Information Research


id: cord-034814-flp6s0wd
type: ORG
entity:
  the Topical Collection Artificial Intelligence Applications for COVID 19 Detection Control Prediction


id: cord-299982-plw0dukq
type: ORG
entity:
  Religion Transport Digital Disease Detection Harnessing the Web for Public Health Surveillance Infodemiology


id: cord-302411-unoiwi4g
type: WORK_OF_ART
entity:
  Priming The Evolution of Three Media Effects Models Framing Toward Clarification of a Fractured Paradigm Switching Trains of Thought


id: cord-302411-unoiwi4g
type: ORG
entity:
  Media Coverage of Public Health Epidemics Linking Framing and Issue Attention Cycle Toward an Integrated Theory of Print News Coverage of Epidemics Treatment


id: cord-315647-isjacgq1
type: ORG
entity:
  a Tool for Health Research A Systematic Review Machine Learning to Detect Self Reporting of Symptoms Testing Access and Recovery Associated With COVID 19


id: cord-169484-mjtlhh5e
type: WORK_OF_ART
entity:
  A Language and Environment for Statistical Computing R Foundation for Statistical Computing Regierungsprotokoll


id: cord-227156-uy4dykhg
type: PROTEIN
entity:
  OfeFernandez PatoBullrich macri macrismo mauriciomacri pichetto MiguelPichetto JuntosPorElCambio alferdez


id: cord-227156-uy4dykhg
type: WORK_OF_ART
entity:
  Proceedings of the 21st International Conference on World Wide Web Use of Machine Learning to Detect Illegal Wildlife Product Promotion and Sales on Twitter Frontiers in Big Data Analyzing


id: cord-320208-uih4jf8w
type: ORG
entity:
  Motion Capture Data View Project Math Information Retrieval View Project Information Theoretical Analysis of


id: cord-344832-0ah4w59o
type: EVENT
entity:
  Great East Japan Earthquake Social Media Communication During Disease Outbreaks Findings and Recommendations


id: cord-344832-0ah4w59o
type: FAC
entity:
  Understanding Communication Ecologies to Bridge Communication Research and Community Action Disaster


id: cord-344832-0ah4w59o
type: ORG
entity:
  Information Systems Frontiers Indigenous Institutions and Their Role in Disaster Risk Reduction and Resilience Evidence


id: cord-344832-0ah4w59o
type: ORG
entity:
  Social Movements as Information Ecologies Exploring the Coevolution of Multiple Internet Technologies for Activism Crowdsourced


id: cord-344832-0ah4w59o
type: ORG
entity:
  a Dynamic Emergency Response Management Journal of Information Technology Theory and Application Social Media


id: cord-344832-0ah4w59o
type: ORG
entity:
  The Instructional Dynamic of Risk and Crisis Communication Distinguishing Instructional Messages from Dialogue Review of Communication Conceptualizing Crisis Communication


# url

id: cord-125817-5o12mbut
domain: github.com
url:
  http://github.com/narcisoyu/Institional-and-news-media-tweet-dataset-for-COVID-19social-science-research


id: cord-180457-047iqerh
domain: www.theguardian.com
url:
  http://www.theguardian.com/world/2020/may/07/black-people-four-times-morelikely-to-die-from-covid-19-ons-finds


id: cord-180457-047iqerh
domain: thefederalist.com
url:
  http://thefederalist.com/2020/03/26/the-scientist-whose-doomsday-pandemicmodel-predicted-armageddon-just-walked-back-the-apocalyptic-predictions/


id: cord-169484-mjtlhh5e
domain: www.derstandard.at
url:
  http://www.derstandard.at/jetzt/livebericht/2000088169126/buwog-prozessvermoegensverwalter-stinksauer-auf-meischberger


id: cord-208179-9pwjnrgl
domain: www.forbes.com
url:
  http://www.forbes.com/sites/tarahaelle/2020/06/19/risking-their-lives-to-save-theirlives-why-public-health-experts-support-black-lives-matter-protests/#11b5ac96851b


id: cord-208179-9pwjnrgl
domain: www.telegraph.co.uk
url:
  http://www.telegraph.co.uk/politics/2020/04/26/lib-dem-councillor-apologisestweeting-photo-bacon-solidarity/


# bib

id: cord-018558-cw9ls112
title: Knowledge-Based Tweet Classification for Disease Sentiment Monitoring
author: Ji, Xiang
date: 2016-03-23
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123466/
doi: 10.1007/978-3-319-30319-2_17
abstract:
  Disease monitoring and tracking is of tremendous value, not only for containing the spread of contagious diseases but also for avoiding unnecessary public concerns and even panic. In this chapter, we present a near real-time sentiment analysis service of public health-related tweets. Traditionally, it is impossible for humans to effectively measure the degree of public health concerns due to limited resources and significant time delays. To solve this problem, we have developed a computational intelligence approach for Epidemic Sentiment Monitoring System (ESMOS) to automatically analyze the disease sentiments and gauge the Measure of Concern (MOC) expressed by Twitter users. More specifically, we present a knowledge-based approach that employs a disease ontology to detect the outbreak of diseases and to analyze the linguistic expressions that convey subjective expressions and sentiment polarity of emotions, feelings, opinions, personal attitudes, etc. with a sentiment classifier. The two-step sentiment classification method utilizes the subjective vocabulary corpus (MPQA), sentiment strength corpus (AFINN), as well as emoticons and profanity words that are often used in social media postings. It first automatically classifies the tweets into personal and non-personal classes, eliminating many tweets such as non-personal “retweets” of news articles from further consideration. In the second stage, the personal tweets are classified into Negative and non-Negative sentiments. In addition, we present a model to quantify the public’s Measure of Concern (MOC) about a disease, based on sentiment classification results. The trends of the public MOC are visualized on a timeline. Correlation analyses between MOC timeline and disease-related sentiment category timelines show that the peaks of the MOC are weakly correlated with the peaks of the News timeline without any appreciable time delay or lead. Our sentiment analysis method and the MOC trend analyses can be generalized to other topical domains, such as mental health monitoring and crisis management. We present the ESMOS prototype for public health-related disease monitoring, for public concern trending and for mapping analyses.


id: cord-018619-aknktp6d
title: A Survey of Social Web Mining Applications for Disease Outbreak Detection
author: Bello-Orgaz, Gema
date: 2015
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123540/
doi: 10.1007/978-3-319-10422-5_36
abstract:
  Social Web Media is one of the most important sources of big data to extract and acquire new knowledge. Social Networks have become an important environment where users provide information of their preferences and relationships. This information can be used to measure the influence of ideas and the society opinions in real time, being very useful on several fields and research areas such as marketing campaigns, financial prediction or public healthcare among others. Recently, the research on artificial intelligence techniques applied to develop technologies allowing monitoring web data sources for detecting public health events has emerged as a new relevant discipline called Epidemic Intelligence. Epidemic Intelligence Systems are nowadays widely used by public health organizations like monitoring mechanisms for early detection of disease outbreaks to reduce the impact of epidemics. This paper presents a survey on current data mining applications and web systems based on web data for public healthcare over the last years. It tries to take special attention to machine learning and data mining techniques and how they have been applied to these web data to extract collective knowledge from Twitter.


id: cord-024385-peakgsyp
title: Social media and moral panics: Assessing the effects of technological change on societal reaction
author: Walsh, James P
date: 2020-03-28
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201200/
doi: 10.1177/1367877920912257
abstract:
  Answering calls for deeper consideration of the relationship between moral panics and emergent media systems, this exploratory article assesses the effects of social media – web-based venues that enable and encourage the production and exchange of user-generated content. Contra claims of their empowering and deflationary consequences, it finds that, on balance, recent technological transformations unleash and intensify collective alarm. Whether generating fear about social change, sharpening social distance, or offering new opportunities for vilifying outsiders, distorting communications, manipulating public opinion, and mobilizing embittered individuals, digital platforms and communications constitute significant targets, facilitators, and instruments of panic production. The conceptual implications of these findings are considered.


id: cord-026173-3a512flu
title: MaTED: Metadata-Assisted Twitter Event Detection System
author: Pandya, Abhinay
date: 2020-05-18
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274331/
doi: 10.1007/978-3-030-50146-4_30
abstract:
  Due to its asynchronous message-sharing and real-time capabilities, Twitter offers a valuable opportunity to detect events in a timely manner. Existing approaches for event detection have mainly focused on building a temporal profile of named entities and detecting unusually large bursts in their usage to signify an event. We extend this line of research by incorporating external knowledge bases such as DBPedia, WordNet; and exploiting specific features of Twitter for efficient event detection. We show that our system utilizing temporal, social, and Twitter-specific features yields improvement in the precision, recall, and DERate on the benchmarked Events2012 corpus compared to the state-of-the-art approaches.


id: cord-026935-586w2cam
author: Fang, Zhichao
date: 2020-06-17
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297939/
doi: 10.1007/s11192-020-03564-9
title:
  An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics

abstract:
  Sufficient data presence is one of the key preconditions for applying metrics in practice. Based on both Altmetric.com data and Mendeley data collected up to 2019, this paper presents a state-of-the-art analysis of the presence of 12 kinds of altmetric events for nearly 12.3 million Web of Science publications published between 2012 and 2018. Results show that even though an upward trend of data presence can be observed over time, except for Mendeley readers and Twitter mentions, the overall presence of most altmetric data is still low. The majority of altmetric events go to publications in the fields of Biomedical and Health Sciences, Social Sciences and Humanities, and Life and Earth Sciences. As to research topics, the level of attention received by research topics varies across altmetric data, and specific altmetric data show different preferences for research topics, on the basis of which a framework for identifying hot research topics is proposed and applied to detect research topics with higher levels of attention garnered on certain altmetric data source. Twitter mentions and policy document citations were selected as two examples to identify hot research topics of interest of Twitter users and policy-makers, respectively, shedding light on the potential of altmetric data in monitoring research trends of specific social attention.


id: cord-027431-6twmcitu
title: Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms
author: Mukhina, Ksenia
date: 2020-05-25
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304753/
doi: 10.1007/978-3-030-50433-5_7
abstract:
  One of the areas that gathers momentum is the investigation of location-based social networks (LBSNs) because the understanding of citizens’ behavior on various scales can help to improve quality of living, enhance urban management, and advance the development of smart cities. But it is widely known that the performance of algorithms for data mining and analysis heavily relies on the quality of input data. The main aim of this paper is helping LBSN researchers to perform a preliminary step of data preprocessing and thus increase the efficiency of their algorithms. To do that we propose a spatiotemporal data processing pipeline that is general enough to fit most of the problems related to working with LBSNs. The proposed pipeline includes four main stages: an identification of suspicious profiles, a background extraction, a spatial context extraction, and a fake transitions detection. Efficiency of the pipeline is demonstrated on three practical applications using different LBSN: touristic itinerary generation using Facebook locations, sentiment analysis of an area with the help of Twitter and VK.com, and multiscale events detection from Instagram posts.


id: cord-029501-syp9ca7t
title: Exploring the components of brand equity amid declining ticket sales in Major League Baseball
author: Merkle, Adam C.
date: 2020-07-21
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372547/
doi: 10.1057/s41270-020-00083-7
abstract:
  Ticket sales for Major League Baseball (MLB) games are decreasing annually, yet baseball fans have increased team interest and following in other ways. Instead of following from the stands or on television fans are choosing to follow, for example, via social media. The emerging unified theory of brand equity offers a framework to examine the mediating role of attendance and local television and the moderating role of Twitter followers on the relationships between MLB marketing assets (MA) and team financial performance. Publicly available secondary data are analyzed with PLS-SEM. The results indicate attendance and local TV partially mediate the relationship between non-seasonal MA and team financial performance, whereas attendance and local TV fully mediate the relationship between in-season MA and team financial performance. Furthermore, the number of Twitter followers for each MLB team moderates various relationships within the MLB brand equity research model. Findings suggest MLB sales and marketing professionals should design ticket sales initiatives that not only promote attendance in the short-term but, more importantly, build upon non-seasonal sources of team brand equity for the long-term. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1057/s41270-020-00083-7) contains supplementary material, which is available to authorized users.


id: cord-032750-sjsju0qp
title: Navigating ‘Home Schooling’ during COVID-19: Australian public response on Twitter
author: Ewing, Lee-Ann
date: 2020-09-24
txt: ./txt/cord-032750-sjsju0qp.txt
cache: ./cache/cord-032750-sjsju0qp.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520655/
doi: 10.1177/1329878x20956409
summary:
  title: Navigating ''Home Schooling'' during COVID-19: Australian public response on Twitter Choice to send children to school lies with families, and distance education packages and resources or online remote learning will be provided to any student who is kept home. Parents can choose not to send their children to school, but are then ''responsible for the student''s learning, safety and wellbeing at home or elsewhere''. We adopt both quantitative (descriptive) and qualitative approaches to analysing the contents of the collected tweets to identify their major themes and concerns of the Australian public in relation to home schooling during the pandemic. Arguably, the frustration over the definition of the learning is highlighting how unsure parents feel in this new role: I came across some online twitter debate on whether it was technically home schooling when in reality you aren''t setting the work.

abstract:
  COVID-19 has wreaked havoc worldwide. Schools have escaped neither the pandemic nor its consequences. Indeed, by April 2020, schools had been suspended in 189 countries, affecting 89% of learners globally. While the Australian government has implemented variously effective health and economic policies in response to COVID-19, their inability to agree with states on education policy during the pandemic caused considerable confusion and anxiety. Accordingly, this study analyses 3 weeks of Tweets during April, leading up to the beginning of term 2, during the height of Government policy incongruity. Findings confirm a wide and rapidly changing range of public responses on Twitter. Nine themes were identified in the quantitative analysis, and six of these (positive, negative, humorous, appreciation for teachers, comments aimed at Government/politicians and definitions) are expanded upon qualitatively. Over the course of 3 weeks, the public began to lose its sense of humour and negative tweets almost doubled.


id: cord-034814-flp6s0wd
title: Design and analysis of a large-scale COVID-19 tweets dataset
author: Lamsal, Rabindra
date: 2020-11-06
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646503/
doi: 10.1007/s10489-020-02029-z
abstract:
  As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset’s geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets’ design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.


id: cord-035254-630w2rtn
title: Using the president’s tweets to understand political diversion in the age of social media
author: Lewandowsky, Stephan
date: 2020-11-10
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655817/
doi: 10.1038/s41467-020-19644-6
abstract:
  Social media has arguably shifted political agenda-setting power away from mainstream media onto politicians. Current U.S. President Trump’s reliance on Twitter is unprecedented, but the underlying implications for agenda setting are poorly understood. Using the president as a case study, we present evidence suggesting that President Trump’s use of Twitter diverts crucial media (The New York Times and ABC News) from topics that are potentially harmful to him. We find that increased media coverage of the Mueller investigation is immediately followed by Trump tweeting increasingly about unrelated issues. This increased activity, in turn, is followed by a reduction in coverage of the Mueller investigation—a finding that is consistent with the hypothesis that President Trump’s tweets may also successfully divert the media from topics that he considers threatening. The pattern is absent in placebo analyses involving Brexit coverage and several other topics that do not present a political risk to the president. Our results are robust to the inclusion of numerous control variables and examination of several alternative explanations, although the generality of the successful diversion must be established by further investigation.


id: cord-102236-z0408dje
title: Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19
author: Dev, Jayati
date: 2020-06-11
url: https://arxiv.org/pdf/2006.06815v1.pdf
doi: 10.13140/rg.2.2.14162.38083
abstract:
  Technology is uniquely positioned to help us analyze large amounts of information to provide valuable insight during widespread public health concerns, like the ongoing COVID-19 pandemic. In fact, information technology companies like Apple and Google have recently launched tools for contact tracing-the ability to process location data to determine the people who have been in contact with a possible patient, in order to contain the spread of the virus. While China and Singapore have successfully led the effort, more and more countries are now implementing such surveillance systems, raising potential privacy concerns about this long term surveillance. For example, it is not clear what happens to the information post-pandemic because people are more likely to share their information during a global crisis without governments having to elaborate on their data policies. Digital Ethnography on Twitter, which has over 330 million users worldwide, with a majority in the United States where the pandemic has the worst effects provides a unique opportunity to learn about real-time opinions of the general public about current affairs in a rather naturalistic setting. Consequently, it might be useful to highlight the privacy concerns of users, should they exist, through analysis of Twitter data and information sharing policies during unprecedented public health outbreaks. This will allow governments to protect their citizens both during and after health emergencies.


id: cord-121200-2qys8j4u
title: Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
author: Zogan, Hamad
date: 2020-07-03
txt: ./txt/cord-121200-2qys8j4u.txt
cache: ./cache/cord-121200-2qys8j4u.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.02847v1.pdf
doi: nan
summary:
  While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. To be specific, this work aims to develop a new novel deep learning-based solution for improving depression detection by utilizing multi-modal features from diverse behaviour of the depressed user in social media. To this end, we propose a hybrid model comprising Bidirectional Gated Recurrent Unit (BiGRU) and Conventional Neural network (CNN) model to boost the classification of depressed users using multi-modal features and word embedding features. The most closely related recent work to ours is [23] where the authors propose a CNN-based deep learning model to classify Twitter users based on depression using multi-modal features.

abstract:
  Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging yet promising to automatically detect mental health problems from such data which is short, sparse and sometimes poorly phrased. However, there are efforts to automatically learn patterns using computational models on such user-generated content. While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts). The CNN model then helps learn useful features. Our experiments show that our model outperforms several popular and strong baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media.


id: cord-123103-pnjt9aa4
title: Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs
author: Ordun, Catherine
date: 2020-05-06
url: https://arxiv.org/pdf/2005.03082v1.pdf
doi: nan
abstract:
  This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.


id: cord-125817-5o12mbut
title: Open access institutional and news media tweet dataset for COVID-19 social science research
author: Yu, Jingyuan
date: 2020-04-03
txt: ./txt/cord-125817-5o12mbut.txt
cache: ./cache/cord-125817-5o12mbut.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.01791v1.pdf
doi: nan
summary:
  title: Open access institutional and news media tweet dataset for COVID-19 social science research On the past Ebola epidemic crisis, scholars found the importance of using Twitter data to do social science research [3] , [4] , many of them use this microblog data as social indicators to analyze the effect of epidemic outbreak on public concerns [5] , health information needs and health seeking behavior [6] , and public response to policy makers [7] etc. Current open access COVID-19 Twitter data were mainly collected by keywords, such as coronavirus, Covid-19 etc [8] , [9] , none of the them is dedicated to government/news media tweet collection. Given that our retrieval targets are policy makers and news source, we believe our dataset can provide scholars more valuable data to conduct social science research in related fields, such as crisis communication, public relation etc. https://github.com/narcisoyu/Institional-and-news-media-tweet-dataset-for-COVID-19social-science-research.

abstract:
  As COVID-19 quickly became one of the most concerned global crisis, the demand for data in academic research is also increasing. Currently, there are several open access Twitter datasets, but none of them is dedicated to the institutional and news media Twitter data collection, to fill this blank, we retrieved data from 69 institutional/news media Twitter accounts, 17 of them were related to government and international organizations, 52 of them were news media across North America, Europe and Asia. We believe our open access data can provide researchers more availability to conduct social science research.


id: cord-131667-zl5txjqx
title: EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets
author: Liu, Junhua
date: 2020-06-09
url: https://arxiv.org/pdf/2006.08369v2.pdf
doi: nan
abstract:
  Since the start of COVID-19, several relevant corpora from various sources are presented in the literature that contain millions of data points. While these corpora are valuable in supporting many analyses on this specific pandemic, researchers require additional benchmark corpora that contain other epidemics to facilitate cross-epidemic pattern recognition and trend analysis tasks. During our other efforts on COVID-19 related work, we discover very little disease related corpora in the literature that are sizable and rich enough to support such cross-epidemic analysis tasks. In this paper, we present EPIC30M, a large-scale epidemic corpus that contains 30 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020. EPIC30M contains a subset of 26.2 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola. Furthermore, we explore and discuss the properties of the corpus with statistics of key terms and hashtags and trends analysis for each subset. Finally, we demonstrate the value and impact that EPIC30M could create through a discussion of multiple use cases of cross-epidemic research topics that attract growing interest in recent years. These use cases span multiple research areas, such as epidemiological modeling, pattern recognition, natural language understanding and economical modeling.


id: cord-135784-ad5avzd6
author: Gharavi, Erfaneh
date: 2020-05-01
url: https://arxiv.org/pdf/2005.00475v1.pdf
doi: nan
title:
  Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US

abstract:
  During a disease outbreak, timely non-medical interventions are critical in preventing the disease from growing into an epidemic and ultimately a pandemic. However, taking quick measures requires the capability to detect the early warning signs of the outbreak. This work collects Twitter posts surrounding the 2020 COVID-19 pandemic expressing the most common symptoms of COVID-19 including cough and fever, geolocated to the United States. Through examining the variation in Twitter activities at the state level, we observed a temporal lag between the rises in the number of symptom reporting tweets and officially reported positive cases which varies between 5 to 19 days.


id: cord-156676-wes5my9e
title: Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
author: Masud, Sarah
date: 2020-10-09
url: https://arxiv.org/pdf/2010.04377v1.pdf
doi: nan
abstract:
  Online hate speech, particularly over microblogging platforms like Twitter, has emerged as arguably the most severe issue of the past decade. Several countries have reported a steep rise in hate crimes infuriated by malicious hate campaigns. While the detection of hate speech is one of the emerging research areas, the generation and spread of topic-dependent hate in the information network remain under-explored. In this work, we focus on exploring user behaviour, which triggers the genesis of hate speech on Twitter and how it diffuses via retweets. We crawl a large-scale dataset of tweets, retweets, user activity history, and follower networks, comprising over 161 million tweets from more than $41$ million unique users. We also collect over 600k contemporary news articles published online. We characterize different signals of information that govern these dynamics. Our analyses differentiate the diffusion dynamics in the presence of hate from usual information diffusion. This motivates us to formulate the modelling problem in a topic-aware setting with real-world knowledge. For predicting the initiation of hate speech for any given hashtag, we propose multiple feature-rich models, with the best performing one achieving a macro F1 score of 0.65. Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention. RETINA achieves a macro F1-score of 0.85, outperforming multiple state-of-the-art models. Our analysis reveals the superlative power of RETINA to predict the retweet dynamics of hateful content compared to the existing diffusion models.


id: cord-164516-qp7k5fz9
author: Goswamy, Tushar
date: 2020-07-30
url: https://arxiv.org/pdf/2007.15619v1.pdf
doi: nan
title:
  AI-based Monitoring and Response System for Hospital Preparedness towards COVID-19 in Southeast Asia

abstract:
  This research paper proposes a COVID-19 monitoring and response system to identify the surge in the volume of patients at hospitals and shortage of critical equipment like ventilators in South-east Asian countries, to understand the burden on health facilities. This can help authorities in these regions with resource planning measures to redirect resources to the regions identified by the model. Due to the lack of publicly available data on the influx of patients in hospitals, or the shortage of equipment, ICU units or hospital beds that regions in these countries might be facing, we leverage Twitter data for gleaning this information. The approach has yielded accurate results for states in India, and we are working on validating the model for the remaining countries so that it can serve as a reliable tool for authorities to monitor the burden on hospitals.


id: cord-169484-mjtlhh5e
title: Dashboard of sentiment in Austrian social media during COVID-19
author: Pellert, Max
date: 2020-06-19
txt: ./txt/cord-169484-mjtlhh5e.txt
cache: ./cache/cord-169484-mjtlhh5e.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.11158v1.pdf
doi: nan
summary:
  To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. The interactive dashboard showcasing our data is available online under http://www.mpellert.at/covid19_monitor_austria/. We gather these data in the form of text from platforms such as Twitter and news forums, where large groups of users discuss timely issues. To fill a gap, we build a dashboard with processed data from three different sources to track the sentiment in Austrian social media during COVID-19. In addition, measures that strongly affect people''s daily lives over a long period of time, as well as high level of uncertainty, likely contribute to the unprecedented changes of collective emotional expression in online social media.

abstract:
  To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. This enables decision makers and the interested public to assess issues such as the attitude towards counter-measures taken during the pandemic and the possible emergence of a (mental) health crisis early on. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online under http://www.mpellert.at/covid19_monitor_austria/. Our work has attracted media attention and is part of an web archive of resources on COVID-19 collected by the Austrian National Library.


id: cord-180457-047iqerh
title: MP Twitter Abuse in the Age of COVID-19: White Paper
author: Gorrell, Genevieve
date: 2020-06-10
url: https://arxiv.org/pdf/2006.08363v1.pdf
doi: nan
abstract:
  As COVID-19 sweeps the globe, outcomes depend on effective relationships between the public and decision-makers. In the UK there were uncivil tweets to MPs about perceived UK tardiness to go into lockdown. The pandemic has led to increased attention on ministers with a role in the crisis. However, generally this surge has been civil. Prime minister Boris Johnson's severe illness with COVID-19 resulted in an unusual peak of supportive responses on Twitter. Those who receive more COVID-19 mentions in their replies tend to receive less abuse (significant negative correlation). Following Mr Johnson's recovery, with rising economic concerns and anger about lockdown violations by influential figures, abuse levels began to rise in May. 1,902 replies to MPs within the study period were found containing hashtags or terms that refute the existence of the virus (e.g. #coronahoax, #coronabollocks, 0.04% of a total 4.7 million replies, or 9% of the number of mentions of"stay home save lives"and variants). These have tended to be more abusive. Evidence of some members of the public believing in COVID-19 conspiracy theories was also found. Higher abuse levels were associated with hashtags blaming China for the pandemic.


id: cord-180835-sgu7ayvw
title: Data-driven modeling of public risk perception and emotion on Twitter during the Covid-19 pandemic
author: Kolic, Blas
date: 2020-08-03
url: https://arxiv.org/pdf/2008.00854v1.pdf
doi: nan
abstract:
  Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from approx. 20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. We find that the national attention on Covid-19 mortality is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity by country to the national Covid-19 death rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.


id: cord-186031-b1f9wtfn
title: Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy
author: Caldarelli, Guido
date: 2020-10-05
url: https://arxiv.org/pdf/2010.01913v1.pdf
doi: nan
abstract:
  During the Covid-19 pandemics, we also experience another dangerous pandemics based on misinformation. Narratives disconnected from fact-checking on the origin and cure of the disease intertwined with pre-existing political fights. We collect a database on Twitter posts and analyse the topology of the networks of retweeters (users broadcasting again the same elementary piece of information, or tweet) and validate its structure with methods of statistical physics of networks. Furthermore, by using commonly available fact checking software, we assess the reputation of the pieces of news exchanged. By using a combination of theoretical and practical weapons, we are able to track down the flow of misinformation in a snapshot of the Twitter ecosystem. Thanks to the presence of verified users, we can also assign a polarization to the network nodes (users) and see the impact of low-quality information producers and spreaders in the Twitter ecosystem.


id: cord-207180-k6f6cmyn
author: Shahrezaye, Morteza
date: 2020-09-27
url: https://arxiv.org/pdf/2009.12905v1.pdf
doi: nan
title:
  COVID-19's (mis)information ecosystem on Twitter: How partisanship boosts the spread of conspiracy narratives on German speaking Twitter

abstract:
  In late 2019, the gravest pandemic in a century began spreading across the world. A state of uncertainty related to what has become known as SARS-CoV-2 has since fueled conspiracy narratives on social media about the origin, transmission and medical treatment of and vaccination against the resulting disease, COVID-19. Using social media intelligence to monitor and understand the proliferation of conspiracy narratives is one way to analyze the distribution of misinformation on the pandemic. We analyzed more than 9.5M German language tweets about COVID-19. The results show that only about 0.6% of all those tweets deal with conspiracy theory narratives. We also found that the political orientation of users correlates with the volume of content users contribute to the dissemination of conspiracy narratives, implying that partisan communicators have a higher motivation to take part in conspiratorial discussions on Twitter. Finally, we showed that contrary to other studies, automated accounts do not significantly influence the spread of misinformation in the German speaking Twitter sphere. They only represent about 1.31% of all conspiracy-related activities in our database.


id: cord-208179-9pwjnrgl
author: Farrell, Tracie
date: 2020-08-12
txt: ./txt/cord-208179-9pwjnrgl.txt
cache: ./cache/cord-208179-9pwjnrgl.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.05261v1.pdf
doi: nan
summary:
  COVID-19 has given rise to malicious content online, including online abuse and hate toward British MPs. In order to understand and contextualise the level of abuse MPs receive, we consider how ministers use social media to communicate about the crisis, and the citizen engagement that this generates. However the correlation is significant across the sample of all MPs. The reaction of the public to the Conservative party and the government''s actions during COVID-19 may be related to the conditions of a public health crisis as discussed in [58, 39] , in which citizens may feel more motivated to trust authorities, although it may also follow from the crisis engaging a different group of people than usually respond to politicians on Twitter. Our third research question asked: Which social media activities of UK MPs during the COVID-19 pandemic receive the most abusive replies?

title:
  Vindication, Virtue and Vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic

abstract:
  COVID-19 has given rise to malicious content online, including online abuse and hate toward British MPs. In order to understand and contextualise the level of abuse MPs receive, we consider how ministers use social media to communicate about the crisis, and the citizen engagement that this generates. The focus of the paper is on a large-scale, mixed methods study of abusive and antagonistic responses to UK politicians during the pandemic from early February to late May 2020. We find that pressing subjects such as financial concerns attract high levels of engagement, but not necessarily abusive dialogue. Rather, criticising authorities appears to attract higher levels of abuse. In particular, those who carry the flame for subjects like racism and inequality, may be accused of virtue signalling or receive higher abuse levels due to the topics they are required by their role to address. This work contributes to the wider understanding of abusive language online, in particular that which is directed at public officials.


id: cord-209697-bfc4h4b3
title: Analyzing Societal Impact of COVID-19: A Study During the Early Days of the Pandemic
author: Shanthakumar, Swaroop Gowdra
date: 2020-10-27
txt: ./txt/cord-209697-bfc4h4b3.txt
cache: ./cache/cord-209697-bfc4h4b3.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.15674v1.pdf
doi: nan
summary:
  We first manually group the hashtags into six main categories, namely, 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope}, and study the temporal evolution of tweets in these hashtags. We adopt a state-of-the-art semantic role labeling approach to identify the action words and then leverage a LSTM-based dependency parsing model to analyze the context of action words (e.g., verb deal is accompanied by nouns such as anxiety, stress, and crisis). We group the hashtags into six main categories, namely 1) General COVID, 2) Quarantine, 3) School Closures, 4) Panic Buying, 5) Lockdowns, and 6) Frustration and Hope to quantitatively and qualitatively understand the chain of events. We develop a Seeded LDA model to categorize tweets into the five hashtag groups: i) General COVID, ii) School Closures, iii) Panic Buying, iv) Lockdowns, and v) Quarantine by seeding each group with seed words from our analysis in Section III-B.

abstract:
  In this paper, we collect and study Twitter communications to understand the societal impact of COVID-19 in the United States during the early days of the pandemic. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. We methodically collect tweets by identifying and tracking trending COVID-related hashtags. We first manually group the hashtags into six main categories, namely, 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope}, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all hashtag groups and specific to each hashtag group and identify the chief concerns of people as the pandemic gripped the nation (e.g., exploring bidets as an alternative to toilet paper). We conduct sentiment analysis and our investigation reveals that people reacted positively to school closures and negatively to the lack of availability of essential goods due to panic buying. We adopt a state-of-the-art semantic role labeling approach to identify the action words and then leverage a LSTM-based dependency parsing model to analyze the context of action words (e.g., verb deal is accompanied by nouns such as anxiety, stress, and crisis). Finally, we develop a scalable seeded topic modeling approach to automatically categorize and isolate tweets into hashtag groups and experimentally validate that our topic model provides a grouping similar to our manual grouping. Our study presents a systematic way to construct an aggregated picture of peoples' response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.


id: cord-211410-7r2xx73n
title: Understanding the Socio-Economic Disruption in the United States during COVID-19's Early Days
author: Shanthakumar, Swaroop Gowdra
date: 2020-04-11
url: https://arxiv.org/pdf/2004.05451v1.pdf
doi: nan
abstract:
  In this paper, we collect and study Twitter communications to understand the socio-economic impact of COVID-19 in the United States during the early days of the pandemic. Our analysis reveals that COVID-19 gripped the nation during this time as is evidenced by the significant number of trending hashtags. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. The communications reveal the ensuing panic buying and the unavailability of some essential goods, in particular toilet paper. We also observe users express their frustration in their communications as the virus spread continued. We methodically collect a total of 530,206 tweets by identifying and tracking trending COVID-related hashtags. We then group the hashtags into six main categories, namely 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all the hashtag groups and specific to each hashtag group. Our preliminary study presents a succinct and aggregated picture of people's response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.


id: cord-217856-4pd1mamv
title: Using Twitter to Analyze Political Polarization During National Crises
author: Shisode, Parth
date: 2020-10-28
url: https://arxiv.org/pdf/2010.15669v1.pdf
doi: nan
abstract:
  Democrats and Republicans have seemed to grow apart in the past three decades. Since the United States as we know it today is undeniably bipartisan, this phenomenon would not appear as a surprise to most. However, there are triggers which can cause spikes in disagreements between Democrats and Republicans at a higher rate than how the two parties have been growing apart gradually over time. This study has analyzed the idea that national events which generally are detrimental to all individuals can be one of those triggers. By testing polarization before and after three events (Hurricane Sandy [2012], N. Korea Missile Test Surge [2019], COVID-19 [2020]) using Twitter data, we show that a measurable spike in polarization occurs between the Democrat and Republican party. In order to measure polarization, sentiments of Twitter users aligned to the Democrat and Republican parties are compared on identical entities (events, people, locations, etc.). Using hundreds of thousands of data samples, a 2.8% increase in polarization was measured during times of crisis compared to times where no crises were occurring. Regardless of the reasoning that the gap between political parties can increase so much during times of suffering and stress, it is definitely alarming to see that among other aspects of life, the partisan gap worsens during detrimental national events.


id: cord-225177-f7i0sbwt
title: Characterizing information leaders in Twitter during COVID-19 crisis
author: Pastor-Escuredo, David
date: 2020-05-14
url: https://arxiv.org/pdf/2005.07266v1.pdf
doi: nan
abstract:
  Information is key during a crisis such as the current COVID-19 pandemic as it greatly shapes people opinion, behaviour and even their psychological state. It has been acknowledged from the Secretary-General of the United Nations that the infodemic of misinformation is an important secondary crisis produced by the pandemic. Infodemics can amplify the real negative consequences of the pandemic in different dimensions: social, economic and even sanitary. For instance, infodemics can lead to hatred between population groups that fragment the society influencing its response or result in negative habits that help the pandemic propagate. On the contrary, reliable and trustful information along with messages of hope and solidarity can be used to control the pandemic, build safety nets and help promote resilience and antifragility. We propose a framework to characterize leaders in Twitter based on the analysis of the social graph derived from the activity in this social network. Centrality metrics are used to identify relevant nodes that are further characterized in terms of users parameters managed by Twitter. We then assess the resulting topology of clusters of leaders. Although this tool may be used for surveillance of individuals, we propose it as the basis for a constructive application to empower users with a positive influence in the collective behaviour of the network and the propagation of information.


id: cord-225887-kr9uljop
title: Covid-19 Tweeting in English: Gender Differences
author: Thelwall, Mike
date: 2020-03-24
url: https://arxiv.org/pdf/2003.11090v1.pdf
doi: nan
abstract:
  At the start of 2020, COVID-19 became the most urgent threat to global public health. Uniquely in recent times, governments have imposed partly voluntary, partly compulsory restrictions on the population to slow the spread of the virus. In this context, public attitudes and behaviors are vitally important for reducing the death rate. Analyzing tweets about the disease may therefore give insights into public reactions that may help guide public information campaigns. This article analyses 3,038,026 English tweets about COVID-19 from March 10 to 23, 2020. It focuses on one relevant aspect of public reaction: gender differences. The results show that females are more likely to tweet about the virus in the context of family, social distancing and healthcare whereas males are more likely to tweet about sports cancellations, the global spread of the virus and political reactions. Thus, women seem to be taking a disproportionate share of the responsibility for directly keeping the population safe. The detailed results may be useful to inform public information announcements and to help understand the spread of the virus. For example, failure to impose a sporting bans whilst encouraging social distancing may send mixed messages to males.


id: cord-227156-uy4dykhg
title: Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter
author: Albanese, Federico
date: 2020-08-24
url: https://arxiv.org/pdf/2008.10749v1.pdf
doi: nan
abstract:
  The formation of majorities in public discussions often depends on individuals who shift their opinion over time. The detection and characterization of these type of individuals is therefore extremely important for political analysis of social networks. In this paper, we study changes in individual's affiliations on Twitter using natural language processing techniques and graph machine learning algorithms. In particular, we collected 9 million Twitter messages from 1.5 million users and constructed the retweet networks. We identified communities with explicit political orientation and topics of discussion associated to them which provide the topological representation of the political map on Twitter in the analyzed periods. With that data, we present a machine learning framework for social media users classification which efficiently detects"shifting users"(i.e. users that may change their affiliation over time). Moreover, this machine learning framework allows us to identify not only which topics are more persuasive (using low dimensional topic embedding), but also which individuals are more likely to change their affiliation given their topological properties in a Twitter graph.


id: cord-235946-6vu34vce
title: Social Cybersecurity Chapter 13: Casestudy with COVID-19 Pandemic
author: Beskow, David M.
date: 2020-08-23
url: https://arxiv.org/pdf/2008.10102v1.pdf
doi: nan
abstract:
  The purpose of this case study is to leverage the concepts and tools presented in the preceding chapters and apply them in a real world social cybersecurity context. With the COVID-19 pandemic emerging as a defining event of the 21st Century and a magnet for disinformation maneuver, we have selected the pandemic and its related social media conversation to focus our efforts on. This chapter therefore applies the tools of information operation maneuver, bot detection and characterization, meme detection and characterization, and information mapping to the COVID-19 related conversation on Twitter. This chapter uses these tools to analyze a stream containing 206 million tweets from 27 million unique users from 15 March 2020 to 30 April 2020. Our results shed light on elaborate information operations that leverage the full breadth of the BEND maneuvers and use bots for important shaping operations.


id: cord-252344-5a0sriq9
title: Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter
author: Saleh, Sameh N.
date: 2020-08-06
url: https://doi.org/10.1017/ice.2020.406
doi: 10.1017/ice.2020.406
abstract:
  OBJECTIVE: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. DESIGN: Retrospective cross-sectional study. METHODS: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. RESULTS: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0–0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). CONCLUSIONS: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation.


id: cord-265704-g3iish7x
title: Dataset on dynamics of Coronavirus on Twitter
author: Aguilar-Gallegos, Norman
date: 2020-05-08
url: https://www.sciencedirect.com/science/article/pii/S2352340920305783?v=s5
doi: 10.1016/j.dib.2020.105684
abstract:
  In this data article, we provide a dataset of 8,982,694 Twitter posts around the coronavirus health global crisis. The data were collected through the Twitter REST API search. We used the rtweet R package to download raw data. The term searched was “Coronavirus” which included the word itself and its hashtag version. We collected the data over 23 days, from January 21 to February 12, 2020. The dataset is multilingual, prevailing English, Spanish, and Portuguese. We include a new variable created from other four variables; it is called “type” of tweets, which is useful for showing the diversity of tweets and the dynamics of users on Twitter. The dataset comprises seven databases which can be analysed separately. On the other hand, they can be crossed to set other researches, among them, trends and relevance of different topics, types of tweets, the embeddedness of users and their profiles, the retweets dynamics, hashtag analysis, as well as to perform social network analysis. This dataset can attract the attention of researchers related to different fields on knowledge, such as data science, social science, network science, health informatics, tourism, infodemiology, and others.


id: cord-269093-x6taxwkx
author: Singh, Amandeep
date: 2019-12-31
txt: ./txt/cord-269093-x6taxwkx.txt
cache: ./cache/cord-269093-x6taxwkx.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/B9780128154588000050
doi: 10.1016/b978-0-12-815458-8.00005-0
summary:
  However, very few review studies have undertaken grouping according to similarities and differences to predict the personality and behavior of individuals with the help of social networking sites such as Facebook, Twitter, and Instagram. However, most of the studies have been done on Twitter, as it is more popular and newer than Facebook and Instagram particularly from 2015 to 2017, and more research needs to be done on other social media spheres in order to analyze the trending behaviors of users. The result section includes a table which provides the research paper analysis according to the year along with pie chart figures, data collection, and behavior analysis methods and classifications based on different methods with line graphs [9] . The results section includes the percentage of research on the three social networking sites, research papers according to year with bar graph representations, data collection and behavior analysis methods and classification based on the different methods with line graph representations.

title:
  5 An Analysis of Demographic and Behavior Trends Using Social Media: Facebook, Twitter, and Instagram

abstract:
  Abstract Personality and character have major effects on certain behavioral outcomes. As advancements in technology occur, more people these days are using social media such as Facebook, Twitter, and Instagram. Due to the increase in social media's popularity, the types of behaviors are now easier to group and study as this is important to know the behavior of users via social networking in order to analyze similarities of certain behavior types and this can be used to predict what they post as well as what they comment, share, and like on social networking sites. However, very few review studies have undertaken grouping according to similarities and differences to predict the personality and behavior of individuals with the help of social networking sites such as Facebook, Twitter, and Instagram. Therefore, the purpose of this research is to collect data from previous researches and to analyze the methods they have used. This chapter reviewed 30 research studies on the topic of behavioral analysis using the social media from 2015 to 2017. This research is based on the method of previous publications and analyzed the results, limitations, and number of users to draw conclusions. Our results indicated that the percentage of completed research on the Facebook, Twitter, and Instagram show that 50% of the studies were done on Twitter, 27% on Facebook, and 23% on Instagram. Twitter seems to be more popular and recent than the other two spheres as there are more studies on it. Further, we extracted the studies based on the year and graphs in 2015 which indicated that more research has been done on Facebook to analyze the behavior of users and the trends are decreasing in the following year. However, more studies have been done on Twitter in 2016 than any other social media. The results also show the classifications based on different methods to analyze individual behavior. However, most of the studies have been done on Twitter, as it is more popular and newer than Facebook and Instagram particularly from 2015 to 2017, and more research needs to be done on other social media spheres in order to analyze the trending behaviors of users. This study should be useful to obtain knowledge about the methods used to analyze user behavior with description, limitations, and results. Although some researchers collect demographic information on users’ gender on Facebook, others on Twitter do not. This lack of demographic data, which is typically available in more traditional sources such as surveys, has created a new focus on developing methods to work out these traits as a means of expanding Big Data research.


id: cord-278119-8k2j3kjv
author: Kawchuk, Greg
date: 2020-06-09
txt: ./txt/cord-278119-8k2j3kjv.txt
cache: ./cache/cord-278119-8k2j3kjv.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32517803/
doi: 10.1186/s12998-020-00319-4
summary:
  From these searches, we determined the magnitude and time course of Twitter activity then coded this activity into content that promoted or refuted a SMT/immunity link. In this descriptive study, we detail how Twitter activity can be used to not only document the magnitude and time course of misinformation describing a link between spinal manipulative therapy (SMT) and boosting immunity, but how social media activity promotes or refutes these claims. These data suggest that the majority of twitter activity regarding a SMT/immunity link is associated with the chiropractic profession with the total number of posts being roughly equal between those promoting and those refuting this link. Direct Twitter activity (posts, likes, retweets, engagement) was similar between tweets promoting and refuting a SMT/immunity link.

title:
  Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis

abstract:
  BACKGROUND: Social media has become an increasingly important tool in monitoring the onset and spread of infectious diseases globally as well monitoring the spread of information about those diseases. This includes the spread of misinformation, which has been documented within the context of the emerging COVID-19 crisis. Understanding the creation, spread and uptake of social media misinformation is of critical importance to public safety. In this descriptive study, we detail Twitter activity regarding spinal manipulative therapy (SMT) and claims it increases, or “boosts”, immunity. Spinal manipulation is a common intervention used by many health professions, most commonly by chiropractors. There is no clinical evidence that SMT improves human immunity. METHODS: Social media searching software (Talkwalker Quick Search) was used to describe Twitter activity regarding SMT and improving or boosting immunity. Searches were performed for the 3 months and 12 months before March 31, 2020 using terms related to 1) SMT, 2) the professions that most often provide SMT and 3) immunity. From these searches, we determined the magnitude and time course of Twitter activity then coded this activity into content that promoted or refuted a SMT/immunity link. Content themes, high-influence users and user demographics were then stratified as either promoting or refuting this linkage. RESULTS: Twitter misinformation regarding a SMT/immunity link increased dramatically during the onset of the COVID crisis. Activity levels (number of tweets) and engagement scores (likes + retweets) were roughly equal between content promoting or refuting a SMT/immunity link, however, the potential reach (audience) of tweets refuting a SMT/immunity link was 3 times higher than those promoting a link. Users with the greatest influence on Twitter, as either promoters or refuters, were individuals, not institutions or organizations. The majority of tweets promoting a SMT/immunity link were generated in the USA while the majority of refuting tweets originated from Canada. CONCLUSION: Twitter activity about SMT and immunity increased during the COVID-19 crisis. Results from this work have the potential to help policy makers and others understand the impact of SMT misinformation and devise strategies to mitigate its impact.


id: cord-281145-pxzsph5v
title: Social Media Mining Toolkit (SMMT)
author: Tekumalla, Ramya
date: 2020-06-15
txt: ./txt/cord-281145-pxzsph5v.txt
cache: ./cache/cord-281145-pxzsph5v.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32634870/
doi: 10.5808/gi.2020.18.2.e16
summary:
  When it comes to using Twitter data for drug identification and pharmacovigilance tasks, authors of works like [7] [8] [9] have been consistently releasing publicly available datasets, software tools, and complete Natural Language Processing (NLP) systems with their works. In an attempt to shift the biomedical community into better practices for research transparency and reproducibility, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating, and standardizing social media data. The need for a toolkit like SMMT arose from our work using Twitter data for the characterization of disease transmission during natural disasters [10] and mining large-scale repositories for drug usage related tweets for pharmacovigilance purposes [11] . After preprocessing the acquired social media data, researchers have the capabilities of standardizing their tweets'' text with our set of tools.

abstract:
  There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data. In order to address this pressing issue, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating and standardizing social media data. The purpose of our toolkit is for researchers to focus on answering research questions, and not the technical aspects of using social media data. By using a standard toolkit, researchers will be able to acquire, use, and release data in a consistent way that is transparent for everybody using the toolkit, hence, simplifying research reproducibility and accessibility in the social media domain.


id: cord-285522-3gv6469y
title: Social big data: Recent achievements and new challenges
author: Bello-Orgaz, Gema
date: 2015-08-28
txt: ./txt/cord-285522-3gv6469y.txt
cache: ./cache/cord-285522-3gv6469y.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.inffus.2015.08.005
doi: 10.1016/j.inffus.2015.08.005
summary:
  Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. Currently, the exponential growth of social media has created serious problems for traditional data analysis algorithms and techniques (such as data mining, statistics, machine learning, and so on) due to their high computational complexity for large datasets. This section provides a description of the basic methods and algorithms related to network analytics, community detection, text analysis, information diffusion, and information fusion, which are the areas currently used to analyse and process information from social-based sources.

abstract:
  Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.


id: cord-287703-1shbiee5
author: Santarone, Kristen
date: 2020-08-31
txt: ./txt/cord-287703-1shbiee5.txt
cache: ./cache/cord-287703-1shbiee5.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1136/tsaco-2020-000496
doi: 10.1136/tsaco-2020-000496
summary:
  The use of hashtags at medical conferences allows material to be discussed and improved on by the experts via online conversation on Twitter. CONCLUSION: Twitter #AAST 2016–2019 online engagement and interactions have declined during the last 4 years while impressions have grown steadily indicating potential widespread dissemination of trauma-related knowledge and evidence-based practices, and increased online utilization of conference material to trauma surgeons, residents and fellows, trauma scientists, other physicians and the lay public. Though tweets and retweets declined, impressions increased significantly from 2016 to 2019, from 5.8 million to 10 million (p<0.05), potentially indicating contents from AAST conference presentations were still being widely viewed and disseminated. Twitter #AAST 2016-2019 online engagement and interactions have declined during the last 4 years while impressions have grown steadily indicating potential widespread dissemination of trauma-related knowledge and evidence-based practices, and increased online utilization of conference material to trauma surgeons, fellows, residents, trauma scientists, other physicians and the lay public.

title:
  Hashtags in healthcare: understanding Twitter hashtags and online engagement at the American Association for the Surgery of Trauma 2016–2019 meetings

abstract:
  BACKGROUND: Social media amplifies the accessibility, reach and impact of medical education and conferences alike. The use of hashtags at medical conferences allows material to be discussed and improved on by the experts via online conversation on Twitter. We aim to investigate the utilization of hashtags at the American Association for the Surgery of Trauma (AAST) meetings from 2016 to 2019 and its potential role in knowledge dissemination and meeting participations. METHODS: Symplur Signals software was used to analyze hashtags for the AAST meetings by year: #AAST2016, #AAST2017, #AAST2018, #AAST2019. RESULTS: Number of tweets decreased significantly from 2016 to 2019 (4500 to 4400 to 3600 to 2600, respectively, p<0.05). Retweets also decreased significantly from 2016 to 2019 (3600 to 3300 to 2600 to 1900, respectively, p<0.05). Users decreased from 2016 to 2019 (1600 to 1400 to 937 to 743, respectively, p<0.05). Despite this decrease, impressions were 5.8 million in 2016, increasing to 8.6 million in 2017, then 9.6 million in 2018 and finally peaking in 2019 where impressions reached 10 million (p<0.05). The top influencer for 2016–2019 was the AAST Twitter account. CONCLUSION: Twitter #AAST 2016–2019 online engagement and interactions have declined during the last 4 years while impressions have grown steadily indicating potential widespread dissemination of trauma-related knowledge and evidence-based practices, and increased online utilization of conference material to trauma surgeons, residents and fellows, trauma scientists, other physicians and the lay public. #AAST online engagement and impressions did not have influence on meeting attendance rates.


id: cord-288195-3lcs77uf
author: Bilal, Mohammad
date: 2020-04-17
url: https://doi.org/10.1055/a-1153-9014
doi: 10.1055/a-1153-9014
title:
  What constitutes urgent endoscopy? A social media snapshot of gastroenterologists’ views during the COVID-19 pandemic

abstract:
  Background and study aims There is a consensus among gastroenterology organizations that elective endoscopic procedures should be deferred during the COVID-19 pandemic. While the decision to perform urgent procedures and to defer entirely elective procedures is mostly evident, there is a wide “middle ground” of time-sensitive but not technically urgent or emergent endoscopic interventions. We aimed to survey gastroenterologists worldwide using Twitter to help elucidate these definitions using commonly encountered clinical scenarios during the COVID-19 pandemic. Methods A 16-question survey was designed by the authors to include common clinical scenarios that do not have clear guidelines regarding the timing or urgency of endoscopic evaluation. This survey was posted on Twitter. The survey remained open to polling for 48 hours. During this time, multiple gastroenterologists and fellows with prominent social media presence were tagged to disseminate the survey. Results The initial tweet had 38,795 impressions with a total of 2855 engagements. There was significant variation in responses from gastroenterologists regarding timing of endoscopy in these semi-urgent scenarios. There were only three of 16 scenarios for which more than 70 % of gastroenterologists agreed on procedure-timing . For example, significant variation was noted in regard to timing of upper endoscopy in patients with melena, with 44.5 % of respondents believing that everyone with melena should undergo endoscopic evaluation at this time. Similarly, about 35 % of respondents thought that endoscopic retrograde cholangiopancreatography should only be performed in patients with choledocholithiasis with abdominal pain or jaundice. Conclusion Our analysis shows that there is currently lack of consensus among gastroenterologists in regards to timing of semi-urgent or non-life-threatening procedures during the COVID-19 pandemic. These results support the need for the ongoing development of societal guidance for these “semi-urgent” scenarios to help gastroenterologists in making difficult triage decisions.


id: cord-297462-c5hafan8
author: Tang, Lu
date: 2018-06-19
txt: ./txt/cord-297462-c5hafan8.txt
cache: ./cache/cord-297462-c5hafan8.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.ajic.2018.05.019
doi: 10.1016/j.ajic.2018.05.019
summary:
  METHOD: This study examined how the public discussed measles during the measles outbreak in the United States during early 2015 that originated in Disneyland Park in Anaheim, CA, through a semantic network analysis of the content of around 1 million tweets using KH coder. 3 This study adds to the research on crisis and emergency risk communication by demonstrating that social media users applied different frames to understand the public health crisis associated with a measles outbreak: news update frame, public health frame, vaccination frame, and political frame. Practically, the findings of the study allow public health professionals to understand how social media users make sense of an EID during different stages of the outbreak so that they can develop more effective crisis communication strategies.

title:
  Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease

abstract:
  BACKGROUND: The public increasingly uses social media not only to look for information about emerging infectious diseases (EIDs), but also to share opinions, emotions, and coping strategies. Identifying the frames used in social media discussion about EIDs will allow public health agencies to assess public opinions and sentiments. METHOD: This study examined how the public discussed measles during the measles outbreak in the United States during early 2015 that originated in Disneyland Park in Anaheim, CA, through a semantic network analysis of the content of around 1 million tweets using KH coder. RESULTS: Four frames were identified based on word frequencies and co-occurrence: news update, public health, vaccination, and political. The prominence of each individual frame changed over the corse of the pre-crisis, initial, maintenance, and resolution stages of the outbreak. CONCLUSIONS: This study proposed and tested a method for assessing the frames used in social media discussions about EIDs based on the creation, interpretation, and quantification of semantic networks. Public health agencies could use social media outlets, such as Twitter, to assess how the public makes sense of an EID outbreak and to create adaptive messages in communicating with the public during different stages of the crisis.


id: cord-299982-plw0dukq
title: Covid19 Surveillance in Peru on April using Text Mining
author: Chire Saire, J. E.
date: 2020-05-25
url: http://medrxiv.org/cgi/content/short/2020.05.24.20112193v1?rss=1
doi: 10.1101/2020.05.24.20112193
abstract:
  The present outbreak as consequence by coronavirus covid19 has generated an big impact over the world. South American countries had their own limitations, challengues and pandemic has highlighted what needs to improve. Peru is a country with good start with quarantine, social distancing policies but the policies was not enough during the weeks. So, the analysis over April is performed through infoveillance using posts from different cities to analyze what population was living or worried during this month. Results presents a high concern about international context, and national situation, besides Economy and Politics are issues to solve. By constrast, Religion and Transport are not very important for peruvian citizens.


id: cord-302411-unoiwi4g
title: Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo
author: Yu, Jingyuan
date: 2020-07-28
url: https://doi.org/10.3390/ijerph17155414
doi: 10.3390/ijerph17155414
abstract:
  While COVID-19 is becoming one of the most severe public health crises in the twenty-first century, media coverage about this pandemic is getting more important than ever to make people informed. Drawing on data scraped from Twitter, this study aims to analyze and compare the news updates of two main Spanish newspapers El País and El Mundo during the pandemic. Throughout an automatic process of topic modeling and network analysis methods, this study identifies eight news frames for each newspaper’s Twitter account. Furthermore, the whole pandemic development process is split into three periods—the pre-crisis period, the lockdown period and the recovery period. The networks of the computed frames are visualized by these three segments. This paper contributes to the understanding of how Spanish news media cover public health crises on social media platforms.


id: cord-303506-rqerh2u3
author: Patel, V.
date: 2020-05-29
url: https://doi.org/10.1101/2020.05.27.20114983
doi: 10.1101/2020.05.27.20114983
title:
  A call for governments to pause Twitter censorship: a cross-sectional study using Twitter data as social-spatial sensors of COVID-19/SARS-CoV-2 research diffusion

abstract:
  Objectives: To determine whether Twitter data can be used as social-spatial sensors to show how research on COVID-19/SARS-CoV-2 diffuses through the population to reach the people that are especially affected by the disease. Design: Cross-sectional bibliometric analysis conducted between 23rd March and 14th April 2020. Setting: Three sources of data were used in the analysis: (1) deaths per number of population for COVID-19/SARS-CoV-2 retrieved from Coronavirus Resource Center at John Hopkins University and Worldometer, (2) publications related to COVID-19/SARS-CoV-2 retrieved from WHO COVID-19 database of global publications, and (3) tweets of these publications retrieved from Altmetric.com and Twitter. Main Outcome(s) and Measure(s): To map Twitter activity against number of publications and deaths per number of population worldwide and in the USA states. To determine the relationship between number of tweets as dependent variable and deaths per number of population and number of publications as independent variables. Results: Deaths per one hundred thousand population for countries ranged from 0 to 104, and deaths per one million population for USA states ranged from 2 to 513. Total number of publications used in the analysis was 1761, and total number of tweets used in the analysis was 751,068. Mapping of worldwide data illustrated that high Twitter activity was related to high numbers of COVID-19/SARS-CoV-2 deaths, with tweets inversely weighted with number of publications. Poisson regression models of worldwide data showed a positive correlation between the national deaths per number of population and tweets when holding the country's number of publications constant (coefficient 0.0285, S.E. 0.0003, p<0.001). Conversely, this relationship was negatively correlated in USA states (coefficient -0.0013, S.E. 0.0001, p<0.001). Conclusions: This study shows that Twitter can play a crucial role in the rapid research response during the COVID-19/SARS-CoV-2 global pandemic, especially to spread research with prompt public scrutiny. Governments are urged to pause censorship of social media platforms during these unprecedented times to support the scientific community's fight against COVID-19/SARS-CoV-2.


id: cord-309790-rx9cux8i
title: Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource
author: Sarker, Abeed
date: 2020-07-04
url: https://doi.org/10.1093/jamia/ocaa116
doi: 10.1093/jamia/ocaa116
abstract:
  OBJECTIVE: To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. MATERIALS AND METHODS: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. RESULTS: We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. CONCLUSION: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.


id: cord-315647-isjacgq1
title: Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter
author: Alanazi, E.
date: 2020-06-12
txt: ./txt/cord-315647-isjacgq1.txt
cache: ./cache/cord-315647-isjacgq1.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.06.10.20127225v1?rss=1
doi: 10.1101/2020.06.10.20127225
summary:
  Objective: The aim of this study is to identify the most common symptoms reported by covid-19 patients in the Arabic language and order the symptoms appearance based on the collected data. For example, Twitter has been the source for data for many health and medical studies; such as surveillance and monitoring of Flu and Cancer timeline and distribution across the USA using Twitter [1] , analyzing the spread of influenza in the UAE based on geotagged Arabic Tweets [2] , surveillance and monitoring of Influenza in the UAE based on Arabic and English tweets [3] , identifying symptoms and disease in Saudi Arabia using Twitter [4] , and most recently on analyzing COVID-19 symptoms on Twitter [5] and analyzing the chronological and geographical distribution of COVID-19 infected tweeters in the USA [6] . Initially, we shuffled Arabic tweets and searching for tweets with COVID-19 symptoms and also collected tweets for users who reported themselves infected through clinical test.

abstract:
  Objective: The aim of this study is to identify the most common symptoms reported by covid-19 patients in the Arabic language and order the symptoms appearance based on the collected data. Methods: We search the Arabic content of Twitter for personal reports of covid-19 symptoms from March 1st to May 27th, 2020. We identify 463 Arabic users who tweeted testing positive for covid-19 and extract the symptoms they publicly associate with covid-19. Furthermore, we ask them directly through personal messages to opt in and rank the appearance of the first three symptoms they experienced right before (or after) diagnosed with covid-19. Finally, we track their Twitter timeline to identify additional symptoms that were mentioned within +-5 days from the day of tweeting having covid-19. In summary, a list of 270 covid-19 reports were collected and symptoms were (at least partially) ranked from early to late. Results: The collected reports contained roughly 900 symptoms originated from 74% (n=201) male and 26% (n=69) female Twitter users. The majority (82%) of the tracked users were living in Saudi Arabia (46%) and Kuwait (36%). Furthermore, 13% (n=36) of the collected reports were asymptomatic. Out of the users with symptoms (n=234), 66% (n=180) provided a chronological order of appearance for at least three symptoms. Fever 59% (n=139), Headache 43% (n=101), and Anosmia 39% (n=91) were found to be the top three symptoms mentioned by the reports. They count also for the top-3 common first symptoms in a way that 28% (n=65) said their covid journey started with a Fever, 15% (n=34) with a Headache and 12% (n=28) with Anosmia. Out of the Saudi symptomatic reported cases (n=110), the most common three symptoms were Fever 59% (n=65), Anosmia 42% (n=46), and Headache 38% (n=42).


id: cord-320208-uih4jf8w
author: Li, Diya
date: 2020-07-10
txt: ./txt/cord-320208-uih4jf8w.txt
cache: ./cache/cord-320208-uih4jf8w.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32664388/
doi: 10.3390/ijerph17144988
summary:
  In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. In this article, we propose a CorExQ9 algorithm that integrates Correlation Explanation (CorEx) learning algorithm and clinical PHQ lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. We assessed the level of stress expressed in COVID-19 related tweets by integrating a lexicon-based method derived from established clinical assessment questionnaire PHQ-9 [46] . The CorEx algorithm combined with clinical stress measure index (PHQ-9) helped to minimize human interventions and human language ambiguity in social media data mining for stress detection and provided accurate stress symptom measures of Twitter users related to the COVID-19 pandemic.

title:
  Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining

abstract:
  By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people’s stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people’s risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.


id: cord-328461-3r5vycnr
title: Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population
author: Chire Saire, J. E.
date: 2020-04-11
url: http://medrxiv.org/cgi/content/short/2020.04.06.20055749v1?rss=1
doi: 10.1101/2020.04.06.20055749
abstract:
  Infoveillance is an application from Infodemiology field with the aim to monitor public health and create public policies. Social sensor is the people providing thought, ideas through electronic communication channels(i.e. Internet). The actual scenario is related to tackle the covid19 impact over the world, many countries have the infrastructure, scientists to help the growth and countries took actions to decrease the impact. South American countries have a different context about Economy, Health and Research, so Infoveillance can be a useful tool to monitor and improve the decisions and be more strategical. The motivation of this work is analyze the capital of Spanish Speakers Countries in South America using a Text Mining Approach with Twitter as data source. The preliminary results helps to understand what happens two weeks ago and opens the analysis from different perspectives i.e. Economics, Social.


id: cord-329999-flzqm3wh
author: Buchanan, Tom
date: 2020-10-07
txt: ./txt/cord-329999-flzqm3wh.txt
cache: ./cache/cord-329999-flzqm3wh.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1371/journal.pone.0239666
doi: 10.1371/journal.pone.0239666
summary:
  Four studies (total N = 2,634) explored the effect of message attributes (authoritativeness of source, consensus indicators), viewer characteristics (digital literacy, personality, and demographic variables) and their interaction (consistency between message and recipient beliefs) on self-reported likelihood of spreading examples of disinformation. Descriptive statistics for participant characteristics (personality, conservatism, new media literacy and age) and their reactions to the stimuli (likelihood of sharing, belief the stories were likely to be true, and rating of likelihood that they had seen them before) are summarised in Table 2 . This evaluated the extent to which digital media literacy (NMLS), authority of the message source, consensus, belief in veracity of the messages, consistency with participant beliefs (operationalised as the total SECS conservatism scale score), age and personality (Extraversion, Conscientiousness, Agreeableness, Openness to Experience and Neuroticism), predicted self-rated likelihood of sharing the posts.

title:
  Why do people spread false information online? The effects of message and viewer characteristics on self-reported likelihood of sharing social media disinformation

abstract:
  Individuals who encounter false information on social media may actively spread it further, by sharing or otherwise engaging with it. Much of the spread of disinformation can thus be attributed to human action. Four studies (total N = 2,634) explored the effect of message attributes (authoritativeness of source, consensus indicators), viewer characteristics (digital literacy, personality, and demographic variables) and their interaction (consistency between message and recipient beliefs) on self-reported likelihood of spreading examples of disinformation. Participants also reported whether they had shared real-world disinformation in the past. Reported likelihood of sharing was not influenced by authoritativeness of the source of the material, nor indicators of how many other people had previously engaged with it. Participants’ level of digital literacy had little effect on their responses. The people reporting the greatest likelihood of sharing disinformation were those who thought it likely to be true, or who had pre-existing attitudes consistent with it. They were likely to have previous familiarity with the materials. Across the four studies, personality (lower Agreeableness and Conscientiousness, higher Extraversion and Neuroticism) and demographic variables (male gender, lower age and lower education) were weakly and inconsistently associated with self-reported likelihood of sharing. These findings have implications for strategies more or less likely to work in countering disinformation in social media.


id: cord-334574-1gd9sz4z
title: Tweeting from the Bench: Twitter and the Physician-Scientist Benefits and Challenges
author: Little, Jessica S.
date: 2020-11-11
url: https://www.ncbi.nlm.nih.gov/pubmed/33179209/
doi: 10.1007/s11899-020-00601-5
abstract:
  PURPOSE OF REVIEW: Social media platforms such as Twitter are increasingly utilized to interact, collaborate, and exchange information within the academic medicine community. However, as Twitter begins to become formally incorporated into professional meetings, educational activities, and even the consideration of academic promotion, it is critical to better understand both the benefits and challenges posed by this platform. RECENT FINDINGS: Twitter use is rising amongst healthcare providers nationally and internationally, including in the field of hematology and oncology. Participation on Twitter at national conferences such as the annual meetings of American Society of Hematology (ASH) and American Society of Clinical Oncology (ASCO) has steadily increased over recent years. Tweeting can be used advantageously to cultivate opportunities for networking or collaboration, promote one’s research and increase access to other’s research, and provide efficient means of learning and educating. However, given the novelty of this platform and little formal training on its use, concerns regarding patient privacy, professionalism, and equity must be considered. SUMMARY: These new technologies present unique opportunities for career development, networking, research advancement, and efficient learning. From “tweet ups” to Twitter journal clubs, physician-scientists are quickly learning how to capitalize on the opportunities that this medium offers. Yet caution must be exercised to ensure that the information exchanged is valid and true, that professionalism is maintained, that patient privacy is protected, and that this platform does not reinforce preexisting structural inequalities.


id: cord-344832-0ah4w59o
title: Disaster-Resilient Communication Ecosystem in an Inclusive Society – A case of foreigners in Japan
author: Sakurai, Mihoko
date: 2020-08-15
txt: ./txt/cord-344832-0ah4w59o.txt
cache: ./cache/cord-344832-0ah4w59o.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.ijdrr.2020.101804
doi: 10.1016/j.ijdrr.2020.101804
summary:
  For future disaster preparedness, we argue that the municipal government, as a responsible agent, should (1) make available instructional information in foreign languages on social media, (2) transfer such information through collaboration with transmitters, and (3) examine the use of local hashtags in social media to strengthen non-Japanese speaker''s capacity to adapt. We regard disaster resilience in the information ecology framework to encompass the efforts of collaboration and communication dependencies that exist amongst stakeholders engaged in the situation within a local context. In this review, social media emerges as a new trend in technology and rather becomes the medium for sharing information with the aim to reduce anxiety about a disaster situation that could negatively affect the people involved [36] . These cases suggest that social media promote effective resilience in communication, and that the delivery of information to foreigners in Japan from different language backgrounds and cultures further creates traits where personal connection contributes to information accessibility choices.

abstract:
  The number of foreign residents and tourists in Japan has been dramatically increasing in recent years. Despite the fact that Japan is prone to natural disasters, with each climate-related event turning into an emergency such as with record rainfalls, floods and mudslides almost every year, non-Japanese communication infrastructure and everyday disaster drills for foreigners have received little attention. This study aims to understand how a resilient communication ecosystem forms in various disaster contexts involving foreigners. Within a framework of information ecology we try to get an overview of the communication ecosystem in literature and outline its structure and trends in social media use. Our empirical case study uses Twitter API and R programming software to extract and analyze tweets in English during Typhoon 19 (Hagibis) in October 2019. It reveals that many information sources transmit warnings and evacuation orders through social media but do not convey a sense of locality and precise instructions on how to act. For future disaster preparedness, we argue that the municipal government, as a responsible agent, should (1) make available instructional information in foreign languages on social media, (2) transfer such information through collaboration with transmitters, and (3) examine the use of local hashtags in social media to strengthen non-Japanese speaker’s capacity to adapt.


id: cord-347459-8ju196uu
author: Nikolovska, Manja
date: 2020-10-21
txt: ./txt/cord-347459-8ju196uu.txt
cache: ./cache/cord-347459-8ju196uu.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1186/s40163-020-00129-2
doi: 10.1186/s40163-020-00129-2
summary:
  Moreover, in terms of the impact of tweets, as measured by the rate at which they are retweeted, followers were more likely to ''spread the word'' when the tweet was content-rich (discussed a crime specific matter and contained media), and account holders were themselves more active on Twitter. The analysis of 114,257 tweets and their metadata indicate that (a) most of the tweets focused on issues that were not specifically about crime; (b) during the time of crisis the stakeholders in question tended to increase their retweeting activity rather than creating original tweets; (c) the visibility of an account (number of followers and favouriting habits) and the richness of the content (discussing Covid-19, crime specific issues and including media such as images) were associated with the likelihood of messages spreading (both in terms of whether they were retweeted and the frequency with which this was so); (d) relative to the preceding 5 months, during the first 5 months of the pandemic tweets on Fraud, Cybercrime and Domestic abuse increased significantly.

title:
  “Show this thread”: policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic

abstract:
  Crisis and disruption are often unpredictable and can create opportunities for crime. During such times, policing may also need to meet additional challenges to handle the disruption. The use of social media by officials can be essential for crisis mitigation and crime reduction. In this paper, we study the use of Twitter for crime mitigation and reduction by UK police (and associated) agencies in the early stages of the Covid-19 pandemic. Our findings suggest that whilst most of the tweets from our sample concerned issues that were not specifically about crime, especially during the first stages of the pandemic, there was a significant increase in tweets about fraud, cybercrime and domestic abuse. There was also an increase in retweeting activity as opposed to the creation of original messages. Moreover, in terms of the impact of tweets, as measured by the rate at which they are retweeted, followers were more likely to ‘spread the word’ when the tweet was content-rich (discussed a crime specific matter and contained media), and account holders were themselves more active on Twitter. Considering the changing world we live in, criminal opportunity is likely to evolve. To help mitigate this, policy makers and researchers should consider more systematic approaches to developing social media communication strategies for the purpose of crime mitigation and reduction during disruption and change more generally. We suggest a framework for so doing.


id: cord-349898-nvi8h77t
title: COVID‐19 pandemic and information diffusion analysis on Twitter
author: Dinh, Ly
date: 2020-10-22
url: https://www.ncbi.nlm.nih.gov/pubmed/33173813/
doi: 10.1002/pra2.252
abstract:
  The COVID‐19 pandemic has impacted all aspects of our lives, including the information spread on social media. Prior literature has found that information diffusion dynamics on social networks mirror that of a virus, but applying the epidemic Susceptible‐Infected‐Removed model (SIR) model to examine how information spread is not sufficient to claim that information spreads like a virus. In this study, we explore whether there are similarities in the simulated SIR model (SIRsim), observed SIR model based on actual COVID‐19 cases (SIRemp), and observed information cascades on Twitter about the virus (INFOcas) by using network analysis and diffusion modeling. We propose three primary research questions: (a) What are the diffusion patterns of COVID‐19 virus spread, based on SIRsim and SIRemp? (b) What are the diffusion patterns of information cascades on Twitter (INFOcas), with respect to retweets, quote tweets, and replies? and (c) What are the major differences in diffusion patterns between SIRsim, SIRemp, and INFOcas? Our study makes a contribution to the information sciences community by showing how epidemic modeling of virus and information diffusion analysis of online social media are distinct but interrelated concepts.


id: cord-356353-e6jb0sex
title: Loops, ladders and links: the recursivity of social and machine learning
author: Fourcade, Marion
date: 2020-08-26
txt: ./txt/cord-356353-e6jb0sex.txt
cache: ./cache/cord-356353-e6jb0sex.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1007/s11186-020-09409-x
doi: 10.1007/s11186-020-09409-x
summary:
  Both practices rely upon and reinforce a pervasive appetite for digital input or feedback that we characterize as "data hunger." They also share a propensity to assemble insight and make meaning accretively-a propensity that we denote here as "world or meaning accretion." Throughout this article, we probe the dynamic interaction of social and machine learning by drawing examples from one genre of online social contention and connection in which the pervasive influence of machine learning is evident: namely, that which occurs across social media channels and platforms. In such settings, the data accretion upon which machine learning depends for the development of granular insights-and, on social media platforms, associated auctioning and targeting of advertising-compounds the cumulative, sedimentary effect of social data, making negative impressions generated by "revenge porn," or by one''s online identity having been fraudulently coopted, hard to displace or renew.

abstract:
  Machine learning algorithms reshape how people communicate, exchange, and associate; how institutions sort them and slot them into social positions; and how they experience life, down to the most ordinary and intimate aspects. In this article, we draw on examples from the field of social media to review the commonalities, interactions, and contradictions between the dispositions of people and those of machines as they learn from and make sense of each other.


