Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. This report is a terse narrative report, and when processing is complete you will be linked to a more complete narrative report. Eric Lease Morgan Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 54 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 6838 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 53 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- 54 Twitter 7 tweet 7 COVID-19 5 user 5 social 5 datum 5 covid-19 4 Facebook 3 network 3 medium 2 topic 2 model 2 abuse 2 Table 2 March 2 Johnson 2 Fig 1 web 1 urgent 1 time 1 team 1 system 1 symptom 1 stress 1 stage 1 share 1 retina 1 republican 1 privacy 1 political 1 platform 1 people 1 parent 1 panic 1 outbreak 1 non 1 news 1 negative 1 moral 1 machine 1 location 1 learning 1 japanese 1 italian 1 information 1 individual 1 home 1 hate 1 group 1 gender Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 2068 tweet 1422 datum 1187 user 1124 medium 834 analysis 830 information 772 topic 767 time 721 network 721 model 658 number 657 word 573 study 544 % 523 research 483 news 416 pandemic 404 result 386 case 385 method 380 event 364 sentiment 363 health 359 hashtag 358 term 351 community 341 dataset 341 account 327 level 312 people 310 system 299 feature 296 content 282 value 282 data 277 outbreak 272 crisis 269 example 269 disease 265 work 262 machine 259 period 258 learning 254 country 247 source 245 group 245 approach 244 algorithm 237 text 236 author Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 1582 Twitter 431 al 426 COVID-19 346 et 196 Fig 181 . 180 March 157 Table 143 News 132 Social 128 twitter 118 Facebook 116 • 114 April 107 United 102 Health 98 Trump 95 States 94 UK 92 China 77 Data 72 Coronavirus 71 Personal 69 LDA 68 May 64 US 61 MLB 60 Figure 58 February 55 Information 53 M 51 SARS 50 Tweet 49 Media 49 Brexit 49 Analysis 48 nan 48 SMT 48 January 47 Russia 47 Instagram 46 u 46 sha 45 t 45 T 45 Johnson 45 English 44 World 44 El 44 Covid-19 Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 2360 we 982 it 410 they 254 i 208 them 101 us 67 you 55 one 50 he 36 themselves 29 itself 16 she 11 him 8 herself 6 ourselves 5 me 4 oneself 4 himself 4 her 3 ours 3 's 2 y 2 u 1 ζ 1 yourself 1 theirs 1 mine 1 m 1 h2b 1 create_dictionary.py Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 8311 be 1618 have 1549 use 580 base 551 show 470 do 425 include 423 relate 379 find 351 identify 345 provide 344 see 273 make 272 contain 270 follow 263 give 251 analyze 246 increase 241 represent 241 consider 230 collect 221 detect 215 present 210 understand 194 compare 193 mention 190 report 188 receive 187 learn 186 create 182 take 179 extract 178 observe 168 generate 165 discuss 157 focus 156 help 155 post 155 apply 153 associate 148 allow 141 perform 139 indicate 138 describe 137 propose 137 appear 135 study 132 retweete 131 share 131 predict Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 1500 social 846 not 739 more 656 also 604 such 599 - 554 other 457 different 439 public 436 high 430 most 350 only 327 political 322 well 320 first 290 large 285 online 268 non 262 new 261 however 254 negative 234 many 231 as 216 then 209 e.g. 201 specific 189 altmetric 186 important 184 covid-19 182 positive 173 same 164 real 161 available 160 low 156 further 155 relevant 149 similar 144 personal 144 early 143 general 141 several 140 even 137 thus 132 local 131 multiple 128 significant 128 likely 127 big 126 therefore 125 various Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 117 most 78 good 61 least 58 high 27 large 18 Most 11 low 11 big 10 great 9 late 7 strong 7 near 6 bad 5 early 4 small 4 short 3 close 2 long 1 weak 1 we.*t 1 simple 1 rich 1 grave 1 few 1 fast 1 eld 1 easy 1 # Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 313 most 33 least 5 well 4 worst Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 34 twitter.com 18 doi.org 8 github.com 4 t.co 3 www.newsguardtech.com 3 www.mpellert.at 3 www.bbc.co.uk 2 www.who.int 2 www.theguardian.com 2 www.tensorflow.org 2 www.derstandard.at 2 osf.io 2 mozdeh.wlv.ac.uk 2 hatebase.org 1 www.telegraph.co.uk 1 www.stratcomcoe.org 1 www.southwalesargus.co.uk 1 www.protezionecivile.gov.it 1 www.promiseskept.com 1 www.pewresearch.org 1 www.panacealab.org 1 www.nhk.or.jp 1 www.nature.com 1 www.mohfw.gov.in 1 www.mirror.co 1 www.jnto.go.jp 1 www.independent.co.uk 1 www.github.com 1 www.forbes.com 1 www.example.com 1 www.easterneye.biz 1 www.bls.gov 1 www.aljazeera.com 1 www.4imn.com 1 webarchiv.onb.ac.at 1 u.afp.com 1 thehill.com 1 thefederalist.com 1 telegraph.co 1 sourceforge.net 1 sarkerlab 1 reporting.aimc.es 1 ow.ly 1 orcid.org 1 muslimnews.co.uk 1 moonshotcve.com 1 metro.co.uk 1 labourlist.org 1 iu.box.com 1 graphviz.org Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 8 http://doi.org/10.1101/2020.05.27.20114983 4 http://doi.org/10.1101/2020.04.06.20055749 2 http://www.tensorflow.org/api 2 http://www.newsguardtech.com/ 2 http://www.mpellert.at/covid19 2 http://twitter.com/MattHancock/status/1264162359733555202 2 http://twitter.com/JackLopresti/status/1247894726486798342 2 http://twitter.com/JackLopresti/status/1247508135029411841 2 http://twitter.com/EdwardJDavey/status/1253882262715842560 2 http://twitter.com/BorisJohnson/status/1238365263764041728 2 http://osf.io/f9bqx/ 2 http://mozdeh.wlv.ac.uk 2 http://hatebase.org 1 http://www.who.int/docs/default-source/coronaviruse/ 1 http://www.who.int 1 http://www.theguardian.com/world/2020/may/07/black-people-four-times-morelikely-to-die-from-covid-19-ons-finds 1 http://www.theguardian.com/global/video/2020/apr/16/ 1 http://www.telegraph.co.uk/politics/2020/04/26/lib-dem-councillor-apologisestweeting-photo-bacon-solidarity/ 1 http://www.stratcomcoe.org/news-hero 1 http://www.southwalesargus.co.uk/news/18444799.wearing-face-covering-mask-walesnot-compulsory/ 1 http://www.protezionecivile.gov.it/ 1 http://www.promiseskept.com/ 1 http://www.pewresearch.org/pathways-2020/COVIDCREATE/main_source_of_ 1 http://www.panacealab.org/covid19 1 http://www.nhk.or.jp/politics/articles/lastweek/25652.html 1 http://www.newsguardtech.com/ratings/rating-process-criteria/ 1 http://www.nature.com/reprintsPublisher's 1 http://www.mpellert.at/covid19_monitor_austria/ 1 http://www.mohfw.gov.in/ 1 http://www.mirror.co 1 http://www.jnto.go.jp/jpn/statistics/visitor_trends/ 1 http://www.independent.co.uk/ 1 http://www.github.com/junhua/epic 1 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 1 http://www.example.com/index.html 1 http://www.easterneye.biz/lib-dem-mps-to-fast-during-ramadan-to-show-unity-formuslim-community/ 1 http://www.derstandard.at/jetzt/livebericht/2000088339740/bundesliga-livelask-sturm 1 http://www.derstandard.at/jetzt/livebericht/2000088169126/buwog-prozessvermoegensverwalter-stinksauer-auf-meischberger 1 http://www.bls.gov/webapps/legacy/cpsatab1.htm 1 http://www.bbc.co.uk/news/stories-52731624 1 http://www.bbc.co.uk/news/election-2019-50246969 1 http://www.bbc.co.uk/ 1 http://www.aljazeera.com/focus/britishelection/2010/05/20105312436485579.html 1 http://www.4imn.com/top200/ 1 http://webarchiv.onb.ac.at/ 1 http://u.afp.com/Z5ab" 1 http://twitter.com/michaelgove/status/1264126108733186050 1 http://twitter.com/lisanandy/status/1237339808017547264 1 http://twitter.com/jeremycorbyn/status/1253341601599852544 1 http://twitter.com/jeremycorbyn/status/1238897340309790721 Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 1 rabindralamsal@outlook.com 1 permissions@acm.org 1 m.nikolovska@ucl.ac.uk Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 5 twitter does not 4 data show different 3 community is more 3 information is available 3 method using profanity 3 model is able 3 time using twitter 3 tweets are often 3 tweets were not 3 twitter is also 3 users are more 2 case is depressed 2 case is not 2 community is mainly 2 covid-19 including cough 2 covid-19 is still 2 data are generally 2 data collected up 2 data do not 2 data is available 2 data is still 2 data were also 2 dataset has more 2 datum is then 2 hashtags related directly 2 information is key 2 media are distinct 2 media is not 2 method does not 2 methods were able 2 model does not 2 number is much 2 results are consistent 2 results are more 2 results presented here 2 study has several 2 terms included wildcard 2 topic does not 2 topics are rarer 2 topics being present 2 tweet contains more 2 tweet is often 2 tweet was boris 2 tweets are also 2 tweets are geo 2 tweets are retweets 2 tweets did not 2 tweets is randomized 2 tweets using natural 2 tweets were more Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 2 case is not depressed 1 analysis was no exception 1 data being not available 1 data do not only 1 media is not always 1 media is not just 1 methods does not properly 1 model does not accurately 1 model is not robust 1 network is not distinguishable 1 networks do not actively 1 results are not materially 1 tweets were not crime 1 twitter are not uniformly 1 twitter is not straightforward A rudimentary bibliography -------------------------- id = cord-265704-g3iish7x author = Aguilar-Gallegos, Norman title = Dataset on dynamics of Coronavirus on Twitter date = 2020-05-08 keywords = Fig; Twitter summary = doi = 10.1016/j.dib.2020.105684 id = cord-315647-isjacgq1 author = Alanazi, E. title = Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter date = 2020-06-12 keywords = Twitter; arabic 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. doi = 10.1101/2020.06.10.20127225 id = cord-227156-uy4dykhg author = Albanese, Federico title = Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter date = 2020-08-24 keywords = Twitter; individual; topic; user summary = doi = nan id = cord-018619-aknktp6d author = Bello-Orgaz, Gema title = A Survey of Social Web Mining Applications for Disease Outbreak Detection date = 2015 keywords = Twitter; disease; web summary = doi = 10.1007/978-3-319-10422-5_36 id = cord-285522-3gv6469y author = Bello-Orgaz, Gema title = Social big data: Recent achievements and new challenges date = 2015-08-28 keywords = Hadoop; Spark; Twitter; big; datum; network; social 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. doi = 10.1016/j.inffus.2015.08.005 id = cord-235946-6vu34vce author = Beskow, David M. title = Social Cybersecurity Chapter 13: Casestudy with COVID-19 Pandemic date = 2020-08-23 keywords = COVID-19; Twitter; account; chinese; figure summary = doi = nan id = cord-288195-3lcs77uf author = Bilal, Mohammad title = What constitutes urgent endoscopy? A social media snapshot of gastroenterologists’ views during the COVID-19 pandemic date = 2020-04-17 keywords = Twitter; covid-19; urgent summary = doi = 10.1055/a-1153-9014 id = cord-329999-flzqm3wh author = Buchanan, Tom title = Why do people spread false information online? The effects of message and viewer characteristics on self-reported likelihood of sharing social media disinformation date = 2020-10-07 keywords = Facebook; Study; Table; Twitter; share 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. doi = 10.1371/journal.pone.0239666 id = cord-186031-b1f9wtfn author = Caldarelli, Guido title = Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy date = 2020-10-05 keywords = Italy; Twitter; community; italian; network; political; user summary = doi = nan id = cord-299982-plw0dukq author = Chire Saire, J. E. title = Covid19 Surveillance in Peru on April using Text Mining date = 2020-05-25 keywords = Health; Twitter summary = doi = 10.1101/2020.05.24.20112193 id = cord-328461-3r5vycnr author = Chire Saire, J. E. title = Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population date = 2020-04-11 keywords = Social; Twitter summary = doi = 10.1101/2020.04.06.20055749 id = cord-102236-z0408dje author = Dev, Jayati title = Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19 date = 2020-06-11 keywords = Twitter; privacy summary = doi = 10.13140/rg.2.2.14162.38083 id = cord-349898-nvi8h77t author = Dinh, Ly title = COVID‐19 pandemic and information diffusion analysis on Twitter date = 2020-10-22 keywords = COVID-19; SIR; Twitter summary = doi = 10.1002/pra2.252 id = cord-032750-sjsju0qp author = Ewing, Lee-Ann title = Navigating ‘Home Schooling’ during COVID-19: Australian public response on Twitter date = 2020-09-24 keywords = Twitter; home; parent 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. doi = 10.1177/1329878x20956409 id = cord-026935-586w2cam author = Fang, Zhichao title = An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics date = 2020-06-17 keywords = Fig; Twitter; altmetric; datum summary = doi = 10.1007/s11192-020-03564-9 id = cord-208179-9pwjnrgl author = Farrell, Tracie title = Vindication, Virtue and Vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic date = 2020-08-12 keywords = Brexit; COVID-19; Johnson; Twitter; abuse; tweet 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? doi = nan id = cord-356353-e6jb0sex author = Fourcade, Marion title = Loops, ladders and links: the recursivity of social and machine learning date = 2020-08-26 keywords = Bourdieu; Facebook; Twitter; datum; learning; machine; medium; people; platform; social; system 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. doi = 10.1007/s11186-020-09409-x id = cord-135784-ad5avzd6 author = Gharavi, Erfaneh title = Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US date = 2020-05-01 keywords = Twitter; outbreak summary = doi = nan id = cord-180457-047iqerh author = Gorrell, Genevieve title = MP Twitter Abuse in the Age of COVID-19: White Paper date = 2020-06-10 keywords = COVID-19; Johnson; Twitter; abuse summary = doi = nan id = cord-164516-qp7k5fz9 author = Goswamy, Tushar title = AI-based Monitoring and Response System for Hospital Preparedness towards COVID-19 in Southeast Asia date = 2020-07-30 keywords = India; Twitter summary = doi = nan id = cord-018558-cw9ls112 author = Ji, Xiang title = Knowledge-Based Tweet Classification for Disease Sentiment Monitoring date = 2016-03-23 keywords = News; Personal; Twitter; negative; non summary = doi = 10.1007/978-3-319-30319-2_17 id = cord-278119-8k2j3kjv author = Kawchuk, Greg title = Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis date = 2020-06-09 keywords = SMT; Twitter; tweet 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. doi = 10.1186/s12998-020-00319-4 id = cord-180835-sgu7ayvw author = Kolic, Blas title = Data-driven modeling of public risk perception and emotion on Twitter during the Covid-19 pandemic date = 2020-08-03 keywords = Fechner; Twitter; affect; covid-19; death summary = doi = nan id = cord-034814-flp6s0wd author = Lamsal, Rabindra title = Design and analysis of a large-scale COVID-19 tweets dataset date = 2020-11-06 keywords = Dataset; Twitter; covid-19; tweet summary = doi = 10.1007/s10489-020-02029-z id = cord-035254-630w2rtn author = Lewandowsky, Stephan title = Using the president’s tweets to understand political diversion in the age of social media date = 2020-11-10 keywords = Mueller; NYT; Russia; Trump; Twitter summary = doi = 10.1038/s41467-020-19644-6 id = cord-320208-uih4jf8w author = Li, Diya title = Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining date = 2020-07-10 keywords = COVID-19; PHQ; Table; Twitter; stress; tweet 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. doi = 10.3390/ijerph17144988 id = cord-334574-1gd9sz4z author = Little, Jessica S. title = Tweeting from the Bench: Twitter and the Physician-Scientist Benefits and Challenges date = 2020-11-11 keywords = Twitter; medium; social summary = doi = 10.1007/s11899-020-00601-5 id = cord-131667-zl5txjqx author = Liu, Junhua title = EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets date = 2020-06-09 keywords = EPIC30; Twitter; epidemic summary = doi = nan id = cord-156676-wes5my9e author = Masud, Sarah title = Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter date = 2020-10-09 keywords = Twitter; hate; model; retina; user summary = doi = nan id = cord-029501-syp9ca7t author = Merkle, Adam C. title = Exploring the components of brand equity amid declining ticket sales in Major League Baseball date = 2020-07-21 keywords = MLB; Twitter; team summary = doi = 10.1057/s41270-020-00083-7 id = cord-027431-6twmcitu author = Mukhina, Ksenia title = Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms date = 2020-05-25 keywords = Twitter; datum; location; user summary = doi = 10.1007/978-3-030-50433-5_7 id = cord-347459-8ju196uu author = Nikolovska, Manja title = “Show this thread”: policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic date = 2020-10-21 keywords = Twitter; covid-19; crime; tweet 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. doi = 10.1186/s40163-020-00129-2 id = cord-123103-pnjt9aa4 author = Ordun, Catherine title = Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs date = 2020-05-06 keywords = Covid19; March; Twitter; time; topic summary = doi = nan id = cord-026173-3a512flu author = Pandya, Abhinay title = MaTED: Metadata-Assisted Twitter Event Detection System date = 2020-05-18 keywords = Twitter; event; tweet summary = doi = 10.1007/978-3-030-50146-4_30 id = cord-225177-f7i0sbwt author = Pastor-Escuredo, David title = Characterizing information leaders in Twitter during COVID-19 crisis date = 2020-05-14 keywords = Twitter; network summary = doi = nan id = cord-303506-rqerh2u3 author = Patel, V. 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 date = 2020-05-29 keywords = SARS; Twitter summary = doi = 10.1101/2020.05.27.20114983 id = cord-169484-mjtlhh5e author = Pellert, Max title = Dashboard of sentiment in Austrian social media during COVID-19 date = 2020-06-19 keywords = Austria; COVID-19; Twitter; datum 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. doi = nan id = cord-344832-0ah4w59o author = Sakurai, Mihoko title = Disaster-Resilient Communication Ecosystem in an Inclusive Society – A case of foreigners in Japan date = 2020-08-15 keywords = Japan; Twitter; disaster; information; japanese 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. doi = 10.1016/j.ijdrr.2020.101804 id = cord-252344-5a0sriq9 author = Saleh, Sameh N. title = Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter date = 2020-08-06 keywords = Twitter; social; tweet summary = doi = 10.1017/ice.2020.406 id = cord-311906-i5i0clgq author = Salik, Jonathan R. title = From Cynic to Advocate: The Use of Twitter in Cardiology date = 2020-08-04 keywords = Twitter summary = doi = 10.1016/j.jacc.2020.06.050 id = cord-287703-1shbiee5 author = Santarone, Kristen title = Hashtags in healthcare: understanding Twitter hashtags and online engagement at the American Association for the Surgery of Trauma 2016–2019 meetings date = 2020-08-31 keywords = AAST; Twitter 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. doi = 10.1136/tsaco-2020-000496 id = cord-309790-rx9cux8i author = Sarker, Abeed title = Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource date = 2020-07-04 keywords = Twitter; symptom summary = doi = 10.1093/jamia/ocaa116 id = cord-207180-k6f6cmyn author = Shahrezaye, Morteza title = COVID-19's (mis)information ecosystem on Twitter: How partisanship boosts the spread of conspiracy narratives on German speaking Twitter date = 2020-09-27 keywords = Twitter; conspiracy; covid-19 summary = doi = nan id = cord-209697-bfc4h4b3 author = Shanthakumar, Swaroop Gowdra title = Analyzing Societal Impact of COVID-19: A Study During the Early Days of the Pandemic date = 2020-10-27 keywords = LDA; Twitter; group 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. doi = nan id = cord-211410-7r2xx73n author = Shanthakumar, Swaroop Gowdra title = Understanding the Socio-Economic Disruption in the United States during COVID-19's Early Days date = 2020-04-11 keywords = March; Twitter summary = doi = nan id = cord-217856-4pd1mamv author = Shisode, Parth title = Using Twitter to Analyze Political Polarization During National Crises date = 2020-10-28 keywords = Democrat; Twitter; republican summary = doi = nan id = cord-269093-x6taxwkx author = Singh, Amandeep title = 5 An Analysis of Demographic and Behavior Trends Using Social Media: Facebook, Twitter, and Instagram date = 2019-12-31 keywords = Facebook; Twitter 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. doi = 10.1016/b978-0-12-815458-8.00005-0 id = cord-297462-c5hafan8 author = Tang, Lu title = Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease date = 2018-06-19 keywords = Twitter; frame; stage 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. doi = 10.1016/j.ajic.2018.05.019 id = cord-281145-pxzsph5v author = Tekumalla, Ramya title = Social Media Mining Toolkit (SMMT) date = 2020-06-15 keywords = SMMT; Twitter 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. doi = 10.5808/gi.2020.18.2.e16 id = cord-225887-kr9uljop author = Thelwall, Mike title = Covid-19 Tweeting in English: Gender Differences date = 2020-03-24 keywords = COVID-19; Twitter; gender summary = doi = nan id = cord-024385-peakgsyp author = Walsh, James P title = Social media and moral panics: Assessing the effects of technological change on societal reaction date = 2020-03-28 keywords = Facebook; Thornton; Twitter; digital; medium; moral; panic; social summary = doi = 10.1177/1367877920912257 id = cord-125817-5o12mbut author = Yu, Jingyuan title = Open access institutional and news media tweet dataset for COVID-19 social science research date = 2020-04-03 keywords = Twitter 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. doi = nan id = cord-302411-unoiwi4g author = Yu, Jingyuan title = Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo date = 2020-07-28 keywords = Madrid; Twitter; news summary = doi = 10.3390/ijerph17155414 id = cord-121200-2qys8j4u author = Zogan, Hamad title = Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media date = 2020-07-03 keywords = CNN; Twitter; feature; model; user 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. doi = nan