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| id | author | title | date | words | sentences | text |
|---|---|---|---|---|---|---|
| cord-265704-g3iish7x | Aguilar-Gallegos, Norman | Dataset on dynamics of Coronavirus on Twitter | 2020-05-08 | nan | nan | view text |
| cord-315647-isjacgq1 | Alanazi, E. | Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter | 2020-06-12 | 2613.0 | 159.0 | view text |
| cord-227156-uy4dykhg | Albanese, Federico | Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter | 2020-08-24 | nan | nan | view text |
| cord-018619-aknktp6d | Bello-Orgaz, Gema | A Survey of Social Web Mining Applications for Disease Outbreak Detection | 2015 | nan | nan | view text |
| cord-285522-3gv6469y | Bello-Orgaz, Gema | Social big data: Recent achievements and new challenges | 2015-08-28 | 13157.0 | 724.0 | view text |
| cord-235946-6vu34vce | Beskow, David M. | Social Cybersecurity Chapter 13: Casestudy with COVID-19 Pandemic | 2020-08-23 | nan | nan | view text |
| cord-288195-3lcs77uf | Bilal, Mohammad | What constitutes urgent endoscopy? A social media snapshot of gastroenterologists’ views during the COVID-19 pandemic | 2020-04-17 | nan | nan | view text |
| cord-329999-flzqm3wh | Buchanan, Tom | Why do people spread false information online? The effects of message and viewer characteristics on self-reported likelihood of sharing social media disinformation | 2020-10-07 | 13812.0 | 728.0 | view text |
| cord-186031-b1f9wtfn | Caldarelli, Guido | Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy | 2020-10-05 | nan | nan | view text |
| cord-299982-plw0dukq | Chire Saire, J. E. | Covid19 Surveillance in Peru on April using Text Mining | 2020-05-25 | nan | nan | view text |
| cord-328461-3r5vycnr | Chire Saire, J. E. | Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population | 2020-04-11 | nan | nan | view text |
| cord-102236-z0408dje | Dev, Jayati | Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19 | 2020-06-11 | nan | nan | view text |
| cord-349898-nvi8h77t | Dinh, Ly | COVID‐19 pandemic and information diffusion analysis on Twitter | 2020-10-22 | nan | nan | view text |
| cord-032750-sjsju0qp | Ewing, Lee-Ann | Navigating ‘Home Schooling’ during COVID-19: Australian public response on Twitter | 2020-09-24 | 3794.0 | 289.0 | view text |
| cord-026935-586w2cam | Fang, Zhichao | An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics | 2020-06-17 | nan | nan | view text |
| cord-208179-9pwjnrgl | Farrell, Tracie | Vindication, Virtue and Vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic | 2020-08-12 | 13384.0 | 665.0 | view text |
| cord-356353-e6jb0sex | Fourcade, Marion | Loops, ladders and links: the recursivity of social and machine learning | 2020-08-26 | 14364.0 | 644.0 | view text |
| cord-135784-ad5avzd6 | Gharavi, Erfaneh | Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US | 2020-05-01 | nan | nan | view text |
| cord-180457-047iqerh | Gorrell, Genevieve | MP Twitter Abuse in the Age of COVID-19: White Paper | 2020-06-10 | nan | nan | view text |
| cord-164516-qp7k5fz9 | Goswamy, Tushar | AI-based Monitoring and Response System for Hospital Preparedness towards COVID-19 in Southeast Asia | 2020-07-30 | nan | nan | view text |
| cord-018558-cw9ls112 | Ji, Xiang | Knowledge-Based Tweet Classification for Disease Sentiment Monitoring | 2016-03-23 | nan | nan | view text |
| cord-278119-8k2j3kjv | Kawchuk, Greg | Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis | 2020-06-09 | 4574.0 | 241.0 | view text |
| cord-180835-sgu7ayvw | Kolic, Blas | Data-driven modeling of public risk perception and emotion on Twitter during the Covid-19 pandemic | 2020-08-03 | nan | nan | view text |
| cord-034814-flp6s0wd | Lamsal, Rabindra | Design and analysis of a large-scale COVID-19 tweets dataset | 2020-11-06 | nan | nan | view text |
| cord-035254-630w2rtn | Lewandowsky, Stephan | Using the president’s tweets to understand political diversion in the age of social media | 2020-11-10 | nan | nan | view text |
| cord-320208-uih4jf8w | Li, Diya | Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining | 2020-07-10 | 8951.0 | 527.0 | view text |
| cord-334574-1gd9sz4z | Little, Jessica S. | Tweeting from the Bench: Twitter and the Physician-Scientist Benefits and Challenges | 2020-11-11 | nan | nan | view text |
| cord-131667-zl5txjqx | Liu, Junhua | EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets | 2020-06-09 | nan | nan | view text |
| cord-156676-wes5my9e | Masud, Sarah | Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter | 2020-10-09 | nan | nan | view text |
| cord-029501-syp9ca7t | Merkle, Adam C. | Exploring the components of brand equity amid declining ticket sales in Major League Baseball | 2020-07-21 | nan | nan | view text |
| cord-027431-6twmcitu | Mukhina, Ksenia | Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms | 2020-05-25 | nan | nan | view text |
| cord-347459-8ju196uu | Nikolovska, Manja | “Show this thread”: policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic | 2020-10-21 | 9390.0 | 442.0 | view text |
| cord-123103-pnjt9aa4 | Ordun, Catherine | Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs | 2020-05-06 | nan | nan | view text |
| cord-026173-3a512flu | Pandya, Abhinay | MaTED: Metadata-Assisted Twitter Event Detection System | 2020-05-18 | nan | nan | view text |
| cord-225177-f7i0sbwt | Pastor-Escuredo, David | Characterizing information leaders in Twitter during COVID-19 crisis | 2020-05-14 | nan | nan | view text |
| cord-303506-rqerh2u3 | Patel, V. | 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 | 2020-05-29 | nan | nan | view text |
| cord-169484-mjtlhh5e | Pellert, Max | Dashboard of sentiment in Austrian social media during COVID-19 | 2020-06-19 | 4672.0 | 272.0 | view text |
| cord-344832-0ah4w59o | Sakurai, Mihoko | Disaster-Resilient Communication Ecosystem in an Inclusive Society – A case of foreigners in Japan | 2020-08-15 | 6686.0 | 359.0 | view text |
| cord-252344-5a0sriq9 | Saleh, Sameh N. | Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter | 2020-08-06 | nan | nan | view text |
| cord-311906-i5i0clgq | Salik, Jonathan R. | From Cynic to Advocate: The Use of Twitter in Cardiology | 2020-08-04 | nan | nan | view text |
| cord-287703-1shbiee5 | Santarone, Kristen | Hashtags in healthcare: understanding Twitter hashtags and online engagement at the American Association for the Surgery of Trauma 2016–2019 meetings | 2020-08-31 | 3084.0 | 192.0 | view text |
| cord-309790-rx9cux8i | Sarker, Abeed | Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource | 2020-07-04 | nan | nan | view text |
| cord-207180-k6f6cmyn | Shahrezaye, Morteza | COVID-19's (mis)information ecosystem on Twitter: How partisanship boosts the spread of conspiracy narratives on German speaking Twitter | 2020-09-27 | nan | nan | view text |
| cord-209697-bfc4h4b3 | Shanthakumar, Swaroop Gowdra | Analyzing Societal Impact of COVID-19: A Study During the Early Days of the Pandemic | 2020-10-27 | 4411.0 | 234.0 | view text |
| cord-211410-7r2xx73n | Shanthakumar, Swaroop Gowdra | Understanding the Socio-Economic Disruption in the United States during COVID-19's Early Days | 2020-04-11 | nan | nan | view text |
| cord-217856-4pd1mamv | Shisode, Parth | Using Twitter to Analyze Political Polarization During National Crises | 2020-10-28 | nan | nan | view text |
| cord-269093-x6taxwkx | Singh, Amandeep | 5 An Analysis of Demographic and Behavior Trends Using Social Media: Facebook, Twitter, and Instagram | 2019-12-31 | 2767.0 | 152.0 | view text |
| cord-297462-c5hafan8 | Tang, Lu | Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease | 2018-06-19 | 4270.0 | 219.0 | view text |
| cord-281145-pxzsph5v | Tekumalla, Ramya | Social Media Mining Toolkit (SMMT) | 2020-06-15 | 2389.0 | 118.0 | view text |
| cord-225887-kr9uljop | Thelwall, Mike | Covid-19 Tweeting in English: Gender Differences | 2020-03-24 | nan | nan | view text |
| cord-024385-peakgsyp | Walsh, James P | Social media and moral panics: Assessing the effects of technological change on societal reaction | 2020-03-28 | nan | nan | view text |
| cord-125817-5o12mbut | Yu, Jingyuan | Open access institutional and news media tweet dataset for COVID-19 social science research | 2020-04-03 | 732.0 | 46.0 | view text |
| cord-302411-unoiwi4g | Yu, Jingyuan | Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo | 2020-07-28 | nan | nan | view text |
| cord-121200-2qys8j4u | Zogan, Hamad | Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media | 2020-07-03 | 10036.0 | 521.0 | view text |