        item: #1 of 54
          id: cord-018558-cw9ls112
      author: Ji, Xiang
       title: Knowledge-Based Tweet Classification for Disease Sentiment Monitoring
        date: 2016-03-23
       words: 9931
      flesch: 54
     summary: (1) We developed a two-step sentiment classification method by combining cluebased labeling and Machine Learning (ML) methods by first automatically labeling the training datasets, and then building classifiers for Personal tweets and classifiers for tweet sentiments. In the first case, News peaks would precede tweet sentiment peaks.
    keywords: classification; concern; data; health; method; negative; news; peaks; personal; public; sentiment; step; timeline; tweets; twitter; words
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        item: #2 of 54
          id: cord-018619-aknktp6d
      author: Bello-Orgaz, Gema
       title: A Survey of Social Web Mining Applications for Disease Outbreak Detection
        date: 2015
       words: 3254
      flesch: 42
     summary: [28] is also a widely data source used for disease outbreaks detection. 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.
    keywords: data; disease; health; mining; outbreaks; public; surveillance; web
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        item: #3 of 54
          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
       words: 6803
      flesch: 24
     summary: For instance, in their recent study of anti-immigrant crusades, Flores-Yeffal et al. (2019) observed how the indexing of social media communications through hashtags like #IllegalsAreCriminals and #WakeUpAmerica fostered networked discourses and connectedness, helping to construct scapegoats, circulate calls for action, and ensure that xenophobic rhetoric echoed throughout cyber-space (see also Morgan and Shaffer, 2017) . key: cord-024385-peakgsyp authors: Walsh, James P title: Social media and moral panics: Assessing the effects of technological change on societal reaction date: 2020-03-28 journal: nan DOI: 10.1177/1367877920912257 sha: doc_id: 24385 cord_uid: peakgsyp 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.
    keywords: claims; communications; content; data; facebook; information; making; media; messages; news; online; panics; platforms; public; research; social; twitter; users
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        item: #4 of 54
          id: cord-026173-3a512flu
      author: Pandya, Abhinay
       title: MaTED: Metadata-Assisted Twitter Event Detection System
        date: 2020-05-18
       words: 3967
      flesch: 56
     summary: Section 3 describes our system for Twitter event detection. [22] advocated the importance of named entities in Twitter event detection.
    keywords: detection; event; phrases; system; text; time; tweets; twitter
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        item: #5 of 54
          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
       words: 9209
      flesch: 37
     summary: Comparing with previous observations of altmetric data coverage reported in earlier altmetric studies, it can be concluded that the presence of altmetric data is clearly increasing, and our results are generally higher than those previous studies using the same types of datasets. Although there are multiple previous studies discussing the coverage of different altmetric data, after nearly 10 years of altmetric research, we find that a renewed large-scale empirical analysis of the up-to-date presence of altmetric data for WoS publications is highly relevant.
    keywords: altmetric; citations; coverage; data; mentions; micro; presence; publications; research; research topics; topics; twitter
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        item: #6 of 54
          id: cord-027431-6twmcitu
      author: Mukhina, Ksenia
       title: Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms
        date: 2020-05-25
       words: 5461
      flesch: 59
     summary: If we look into any popular LBSN, like Instagram or Twitter, location data contains a lot of errors [5] . After that, we calculate the difference between titles in social media data and data from OSM with the help of Damerau-Levenshtein distance.
    keywords: area; city; data; location; number; places; posts; tags; twitter; users
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        item: #7 of 54
          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
       words: 9765
      flesch: 47
     summary: 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. Based on our findings, we offer support for the emerging unified theory of brand equity, discuss implications associated with low attendance, and suggest how salespeople could further the goal of building team brand equity.
    keywords: assets; attendance; brand; equity; financial; game; marketing; marketing assets; mlb; non; performance; relationship; season; team; twitter; value; viewership
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        item: #8 of 54
          id: cord-032750-sjsju0qp
      author: Ewing, Lee-Ann
       title: Navigating ‘Home Schooling’ during COVID-19: Australian public response on Twitter
        date: 2020-09-24
       words: 3797
      flesch: 65
     summary: This study focused on analysing public opinions about home schooling in Australia; therefore, we provided a search query (homeschooling OR 'home schooling') to the Twitter Search function of the API. 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.
    keywords: home; homeschooling; learning; parents; public; schooling; teachers; tweets; twitter
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        item: #9 of 54
          id: cord-034814-flp6s0wd
      author: Lamsal, Rabindra
       title: Design and analysis of a large-scale COVID-19 tweets dataset
        date: 2020-11-06
       words: 5521
      flesch: 55
     summary: Saving thousands of tweets information every minute triggers continuous writing activity on the disk. Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs, Direct Message IDs and/or User IDs.
    keywords: analysis; covid-19; dataset; geo; hashtags; ids; information; pandemic; sentiment; study; tweets; twitter
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        item: #10 of 54
          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
       words: 8491
      flesch: 45
     summary: Similarly, media coverage has been identified as a driver-rather than an echo-of public support for right-wing populist parties in the UK 8 . Each of the two paths is represented by a regression model that relates Twitter activity (represented by the number of relevant tweets) with media coverage (represented by the number of reports concerning Russia-Mueller).
    keywords: analysis; coverage; diversion; keywords; media; media coverage; model; mueller; news; president; suppression; trump; tweets; twitter
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        item: #11 of 54
          id: cord-102236-z0408dje
      author: Dev, Jayati
       title: Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19
        date: 2020-06-11
       words: 2421
      flesch: 48
     summary: Ethics: I chose not to collect Twitter user information in order to make the dataset de-identified and protect user privacy. Consequently, it might be useful to highlight privacy concerns of users [6] , should they exist, through analysis of Twitter data and information sharing policies during unprecedented public health outbreaks.
    keywords: concerns; information; privacy; surveillance; tweets
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        item: #12 of 54
          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
       words: 10040
      flesch: 46
     summary: This motivates us to leverage the superior neural network learning capability with the rich and heterogeneous behavioural patterns of social media users. (3) We built a CNN network to classify user timeline posts concatenated with BiGRU network to identify social media users who suffer from depression.
    keywords: authors; data; depression; detection; features; information; learning; media; model; multi; tweets; user; word
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        item: #13 of 54
          id: cord-123103-pnjt9aa4
      author: Ordun, Catherine
       title: Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs
        date: 2020-05-06
       words: 6983
      flesch: 59
     summary: 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. large scale analytics on factors impacting retweet in twitter network An algorithm for drawing general undirected graphs Virality prediction and community structure in social networks Share of u.s. adults using social media, including facebook, is mostly unchanged since How twitter users compare to the general public Retweets are trash Characterizing diabetes, diet, exercise, and obesity comments on twitter Comparing twitter and traditional media using topic models Empirical study of topic modeling in twitter Characterizing twitter discussions about hpv vaccines using topic modeling and community detection Topic modeling in twitter:
    keywords: analysis; corpus; covid19; et al; figure; network; retweet; retweeting; time; topics; tweets; twitter; umap
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        item: #14 of 54
          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
       words: 732
      flesch: 45
     summary: 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. On the past Ebola epidemic crisis, scholars found the importance of using Twitter data to do social science research
    keywords: news; tweet; twitter
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        item: #15 of 54
          id: cord-131667-zl5txjqx
      author: Liu, Junhua
       title: EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets
        date: 2020-06-09
       words: 4080
      flesch: 43
     summary: In time of crisis caused by epidemics, we realize the necessity of rigorous arrangements, quick responses, credible and updated information during the premature phases of such epidemics [40] . 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.
    keywords: analysis; corpora; crisis; data; epic30; epidemic; time; tweets; twitter
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        item: #16 of 54
          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
       words: 2498
      flesch: 52
     summary: In this paper, we explore Twitter data right before and during COVID-19 pandemic across the United States at the state level, for the most common symptoms of COVID-19 including cough and fever. To offer a framework for outbreak early detection, the result of analysis on Twitter data are compared to the formal dataset provided by John Hopkins University which is openly available to the public for educational and academic research purposes 3 .
    keywords: covid-19; data; outbreak; states; tweets; twitter
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        item: #17 of 54
          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
       words: 8730
      flesch: 53
     summary: However, they do not tackle the problem of modeling the diffusion and restrict themselves to identifying different characteristics of hate speech in Gab. Hate speech on Twitter: Twitter, as one of the largest micro-blogging platforms with a worldwide user base, has a long history of accommodating hate speech, cyberbullying, and toxic behavior. In this work, we choose to focus on the dynamics of hate speech on Twitter mainly due to two reasons: (i) the wide-spread usage of Twitter compared to other platforms provides scope to grasp the hate diffusion dynamics in a more realistic manifestation, and (ii) understanding how hate speech emerges and spreads even in the presence of some top-down checking measures, compared to unmoderated platforms like Gab. Diffusion patterns of hate vs. non-hate on Twitter: Hate speech is often characterized by the formation of echochambers, i.e., only a small group of people engaging with such contents repeatedly.
    keywords: diffusion; features; hate; hate speech; information; models; prediction; retina; retweet; speech; tweets; twitter; user
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        item: #18 of 54
          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
       words: 2714
      flesch: 56
     summary: 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. To the best of our knowledge, this is the first and the only approach of its kind, which can detect the trends in the worst-hit regions accurately based on Twitter data.
    keywords: data; hospitals; india; model; tweets; twitter
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        item: #19 of 54
          id: cord-169484-mjtlhh5e
      author: Pellert, Max
       title: Dashboard of sentiment in Austrian social media during COVID-19
        date: 2020-06-19
       words: 4673
      flesch: 52
     summary: We adapt the dictionaries to the task at hand by excluding most obvious terms that can bias the analysis, as done in recent research validating Twitter word frequency data [18] . The sentiment dynamics on social media platforms can be influenced by content that spreads fear and other negative emotions.
    keywords: austria; covid-19; dashboard; data; derstandard.at; livetickers; online; platform; posts; public; twitter
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        item: #20 of 54
          id: cord-180457-047iqerh
      author: Gorrell, Genevieve
       title: MP Twitter Abuse in the Age of COVID-19: White Paper
        date: 2020-06-10
       words: 5692
      flesch: 56
     summary: Higher abuse levels were associated with hashtags blaming China for the pandemic. [6] In previous studies, we have found Conservatives receiving higher abuse levels, yet here we see Labour politicians receiving more abuse in most periods.
    keywords: abuse; attention; covid-19; high; levels; mps; period; replies; table; tweets; twitter; virus
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        item: #21 of 54
          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
       words: 8288
      flesch: 47
     summary: key: cord-180835-sgu7ayvw authors: Kolic, Blas; Dyer, Joel title: Data-driven modeling of public risk perception and emotion on Twitter during the Covid-19 pandemic date: 2020-08-03 journal: nan DOI: nan sha: doc_id: 180835 cord_uid: sgu7ayvw 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.
    keywords: affect; countries; country; covid-19; data; death; law; number; pandemic; perception; public; risk; twitter; words
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        item: #22 of 54
          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
       words: 12581
      flesch: 49
     summary: It is interesting to see the relative importance of hashtags intended to encourage the population during the lockdown: it is the case of #celafaremo (we will make it), #iorestoacasa (I am staying home), #fermiamoloinsieme (Let's stop it together ): #iorestoacasa is present in every community, but it ranks 13th in the M5S verified user community, 29th in the FI-L-FdI community, 2nd in the Italia Viva community and 10th in the PD one. As a final task, over the whole set of tweets produced or shared by the users in the directed validated network, we counted the number of times a message containing a url was shared by users belonging to different political communities, although without considering the semantics of the tweets.
    keywords: accounts; bipartite; blue; case; center; communities; community; covid-19; domains; information; italian; network; news; non; number; right; table; tweets; twitter; users; wing
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        item: #23 of 54
          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
       words: 3578
      flesch: 43
     summary: In terms of content moderation by Twitter, on average 7.3% of conspiracy narrative tweets are deleted after a certain period of time which is significantly higher than 6% of tweets in the control group. Based on this analysis, 1.31% of COVID-19 conspiracy narrative tweets are suspected to be posted by automated accounts.
    keywords: conspiracy; covid-19; narratives; tweets; twitter; users
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        item: #24 of 54
          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
       words: 13386
      flesch: 51
     summary: 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. # Replies is the number of replies tweets by that demographic in the qualitative corpus received, and # Abusive is the number of those replies that were abusive (recall that the tweet is only included if it receives a high level of abuse).
    keywords: abuse; covid-19; crisis; government; hate; health; language; media; mps; online; pandemic; party; politicians; public; replies; tweets; twitter; women; work
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        item: #25 of 54
          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
       words: 4412
      flesch: 54
     summary: 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). For instance, in School Closures, the action word close mostly talks about closing the schools for benefit of students, and action word offer co-occurs with teaching aids through online sources.
    keywords: analysis; data; group; hashtags; model; tweets; twitter; words
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        item: #26 of 54
          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
       words: 3112
      flesch: 56
     summary: Though Twitter data has previously been shown to be biased [7] , Twitter has emerged as the primary media for people to express their opinion especially during this time and our study offers a perspective into the impact as self-disclosed by people in a form that is easily understandable and can be acted upon. As is the case with most studies based on Twitter data, we also acknowledge the presence of bias in data collection
    keywords: covid-19; hashtags; tweets; twitter; words
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        item: #27 of 54
          id: cord-217856-4pd1mamv
      author: Shisode, Parth
       title: Using Twitter to Analyze Political Polarization During National Crises
        date: 2020-10-28
       words: 4463
      flesch: 48
     summary: In this study, two data sets of entity sentiments were used, with one set coming from Democrat users and the other from Republican users. The effect on COVID-19 lockdowns on political support: Some good news for democracy Suite 800 Washington, & Inquiries, D. 20036USA202-419-4300 | M Most Americans Say Trump Was Too Slow in Initial Response to Coronavirus Threat Analyzing political sentiment on twitter When Public Tragedies Happen: Community Practice Approaches in Grief, Loss, and Recovery Analyzing Economic behavior and the partisan perceptual screen Partisanship and economic behavior: Do partisan differences in economic forecasts predict real economic behavior From individual to community: The framing of 4-16 and the display of social solidarity The effect of a natural disaster on social cohesion: A longitudinal study Suite 800Washington, & Inquiries, D. 20036USA202-419-4300 | M Americans' growing partisan divide: 8 key findings Measuring polarization in highdimensional data: Method and application to congressional speech Inferring User Political Preferences from Streaming Communications Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter
    keywords: data; entities; entity; polarization; republican; sentiment; study
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        item: #28 of 54
          id: cord-225177-f7i0sbwt
      author: Pastor-Escuredo, David
       title: Characterizing information leaders in Twitter during COVID-19 crisis
        date: 2020-05-14
       words: 2437
      flesch: 46
     summary: Infodemics are frequent specially in social networks that are distributed systems of information generation and spreading. Social network analysis as a pioneering tool to investigate shared leadership within sports teams Discovering leaders from community actions Analyzing World Leaders Interactions on Social Media We would like to thank the Center of Innovation and Technology for Development at Technical University Madrid for support and valuable input, specially to Xose Ramil, Sara Romero and Mónica del Moral.
    keywords: centrality; information; network; nodes; users
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        item: #29 of 54
          id: cord-225887-kr9uljop
      author: Thelwall, Mike
       title: Covid-19 Tweeting in English: Gender Differences
        date: 2020-03-24
       words: 3007
      flesch: 51
     summary: Comparing (a) with (b) gives an indication of likely gender differences overall. It focuses on one relevant aspect of public reaction: gender differences.
    keywords: covid-19; differences; gender; males; public; tweets; word
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        item: #30 of 54
          id: cord-227156-uy4dykhg
      author: Albanese, Federico
       title: Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter
        date: 2020-08-24
       words: 4943
      flesch: 45
     summary: This is also consistent with the fact that the model trained with network features gets a better AU C than the model trained with the texts of user tweets in all datasets. Some other works are focused in political analysis and the interaction between users, as for instance the one of Aruguete et al., which described how Twitter users frame political events by sharing content exclusively with likeminded users forming two well-defined communities [12] .
    keywords: communities; individuals; learning; machine; network; time; topics; tweets; twitter; users
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        item: #31 of 54
          id: cord-235946-6vu34vce
      author: Beskow, David M.
       title: Social Cybersecurity Chapter 13: Casestudy with COVID-19 Pandemic
        date: 2020-08-23
       words: 8974
      flesch: 58
     summary: We see a modest number of state sponsored tweets, with substantially more amplification of state sponsored accounts. It is often important to evaluate the quantity and nature of suspended accounts in any event oriented stream.
    keywords: accounts; analysis; bot; chinese; covid-19; data; figure; information; media; memes; network; operations; state; stream; twitter; use
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        item: #32 of 54
          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
       words: 3669
      flesch: 47
     summary: The hashtags #socialdistancing and #stayathome, which were the top trending social distancing hashtags at the time of data extraction, were used to identify tweets related to social distancing. We studied public perception of social distancing through organic, large-scale discussion on Twitter.
    keywords: analysis; distancing; hashtags; public; sentiment; socialdistancing; topics; tweets; twitter
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        item: #33 of 54
          id: cord-265704-g3iish7x
      author: Aguilar-Gallegos, Norman
       title: Dataset on dynamics of Coronavirus on Twitter
        date: 2020-05-08
       words: 2906
      flesch: 70
     summary: Hashtag Freq. weighted since pairs of nodes could be linked in several times through different tweets. The links are References Return of the Coronavirus: 2019-nCoV Pandemics in the age of twitter: content analysis of tweets during the 2009 H1N1 outbreak Zika in twitter: temporal variations of locations, actors, and concepts Data on sentiments and emotions of olympic-themed tweets Developer agreement and policy rtweet: Collecting and analyzing Twitter data Want to be retweeted?
    keywords: coronavirus; data; fig; tweets; twitter
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        item: #34 of 54
          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
       words: 2768
      flesch: 43
     summary: Privacy is major concern Algorithm for news feed is not known Filtering is not done properly [16] It has been observed that individuals who are friends with each others have similar interests Two evaluation metrics were used to judge the performance of classifier ROC and PR used to The aim of this research is to know the methods used by researchers to predict the behavior of social media users. This analysis from the reported studies gives an overview of methods used to predict the personality of social media users.
    keywords: analysis; behavior; data; media; research; users
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        item: #35 of 54
          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
       words: 4574
      flesch: 47
     summary: The majority of tweets promoting a SMT/immunity link were generated in the USA while the majority of refuting tweets originated from Canada. Although both promoting and refuting tweets were similar in their engagement scores (3319 vs. 3590), refuting tweets had a potential reach that was 3 times greater than promoting tweets (4,626,820 vs. 1,558,937).
    keywords: activity; immunity; link; media; misinformation; search; smt; tweets; twitter
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        item: #36 of 54
          id: cord-281145-pxzsph5v
      author: Tekumalla, Ramya
       title: Social Media Mining Toolkit (SMMT)
        date: 2020-06-15
       words: 2391
      flesch: 48
     summary: Genomics Inform DOI: 10.5808/gi.2020.18.2.e16 sha: doc_id: 281145 cord_uid: pxzsph5v 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.
    keywords: data; media; smmt; tools; tweets; twitter
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        item: #37 of 54
          id: cord-285522-3gv6469y
      author: Bello-Orgaz, Gema
       title: Social big data: Recent achievements and new challenges
        date: 2015-08-28
       words: 13157
      flesch: 45
     summary: key: cord-285522-3gv6469y authors: Bello-Orgaz, Gema; Jung, Jason J.; Camacho, David title: Social big data: In this paper, we assume that social big data comes from joining the efforts of the two previous domains: social media and big data.
    keywords: algorithms; analysis; applications; clustering; data; graph; hadoop; information; learning; machine; mapreduce; media; methods; mining; networks; number; processing; spark; system; techniques; text; time; twitter; users
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        item: #38 of 54
          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
       words: 3085
      flesch: 43
     summary: Future studies should investigate the long-term effects of conference hashtag use on knowledge dissemination after the conclusion of the conference. key: cord-287703-1shbiee5 authors: Santarone, Kristen; Boneva, Dessy; McKenney, Mark; Elkbuli, Adel 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 journal: Trauma Surg Acute Care Open DOI: 10.1136/tsaco-2020-000496 sha: doc_id: 287703 cord_uid: 1shbiee5 BACKGROUND:
    keywords: aast; impressions; media; meeting; trauma; twitter; use
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        item: #39 of 54
          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
       words: 3271
      flesch: 39
     summary: Semi-urgent endoscopy was defined as a procedure that could reasonably be deferred for at least 8 weeks without negatively-impacting an important patient outcome (e. g. upstaging of a new cancer diagnosis). In addition, the COVID-19 pandemic is in different phases throughout the world and as the crisis worsens, the definition of semi-urgent endoscopy may narrow.
    keywords: endoscopic; gastroenterologists; pandemic; patients; survey; urgent
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        item: #40 of 54
          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
       words: 4272
      flesch: 52
     summary: 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. Four distinct frames were identified inductively based on the reading of the semantic networks and tweets containing the key words included in these semantic networks: news update frame, public health frame, vaccine frame, and political frame.
    keywords: frame; measles; media; news; outbreak; public; stage; tweets
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        item: #41 of 54
          id: cord-299982-plw0dukq
      author: Chire Saire, J. E.
       title: Covid19 Surveillance in Peru on April using Text Mining
        date: 2020-05-25
       words: 1643
      flesch: 44
     summary: A neurological perspective Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study Twitter as a tool for health research: a systematic review Social media as a tool to increase the impact of public health research Investigating public health surveillance using twitter Pandemics in the age of twitter: content analysis of tweets during the 2009 h1n1 outbreak Building intelligent indicators to detect dengue epidemics in brazil using social networks What is the people posting about symptoms related to coronavirus in bogota, colombia Infoveillance based on social sensors to analyze the impact of covid19 in south american population Text mining approach to analyze coronavirus impact: Mexico city as case of study Towards effective emerging infectious diseases surveillance: Evidence from kenya, peru, thailand, and the u.s.-mexico In this field, many ways of public health analysis appear, among them, infodemiology, an emerging area of research studying the relationship between information technology and consumer health, as well as the tools of infometrics and web analysis whose final objective is to inform and collaborate with public health and public policies [2] .
    keywords: analysis; health; information; research; twitter
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        item: #42 of 54
          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
       words: 5375
      flesch: 49
     summary: Empirical studies about news frames have been conducted during the past epidemic crisis. The decision made on the number of topics is because too few topics make news frames less specific and too many topics make the network less interpretable [40] .
    keywords: crisis; frames; health; information; media; network; news; news frames; pandemic; period; twitter
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        item: #43 of 54
          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
       words: 2935
      flesch: 47
     summary: key: cord-303506-rqerh2u3 authors: Patel, V.; Haunschild, R.; Bornmann, L.; Garas, G. 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 journal: nan DOI: 10.1101/2020.05.27.20114983 sha: doc_id: 303506 cord_uid: rqerh2u3 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. • Using Twitter data used as social-spatial sensors, we demonstrated that Twitter activity was significantly positively correlated to the numbers of COVID-19/SARS-CoV-2 deaths, when holding the country's number of publications constant.
    keywords: license; medrxiv; number; preprint; twitter
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        item: #44 of 54
          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
       words: 2690
      flesch: 48
     summary: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings. To the best of our knowledge, this is the first study that focuses on extracting COVID-19 symptoms from public social media.
    keywords: covid-19; studies; symptoms; tweets; twitter; users
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        item: #45 of 54
          id: cord-311906-i5i0clgq
      author: Salik, Jonathan R.
       title: From Cynic to Advocate: The Use of Twitter in Cardiology
        date: 2020-08-04
       words: 448
      flesch: 46
     summary: Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact More than likes and tweets: creating social media portfolios for academic promotion and tenure The Kardashian index: a measure of discrepant social media profile for scientists While the majority of Americans continue to use social media for personal communication, individuals have increasingly begun to utilize social media as a primary source of news.
    keywords: media; twitter
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        item: #46 of 54
          id: cord-315647-isjacgq1
      author: Alanazi, E.
       title: Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter
        date: 2020-06-12
       words: 2614
      flesch: 59
     summary: Methods: We search the Arabic content of Twitter for personal reports of covid-19 symptoms from March 1st to May 27th, 2020. One potential benefit of analyzing social networks is understanding covid-19 symptoms and identifying people at high risk [7] .
    keywords: covid-19; reports; symptoms; twitter; users
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        item: #47 of 54
          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
       words: 8953
      flesch: 53
     summary: 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 Even though our datasets were preprocessed and selected with entities on COVID-19 related topic, some of the tweets might be outside of the topic or are influenced by other objective factors.
    keywords: accuracy; algorithm; analysis; assessment; covid-19; data; number; phq; results; stress; symptoms; topic; tweets; twitter; words
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        item: #48 of 54
          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
       words: 1769
      flesch: 49
     summary: 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. This virus had a fast growth of infections in China, Italu and many countries in Asia, Europe during January and February.
    keywords: countries; country; data; license; preprint; twitter
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        item: #49 of 54
          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
       words: 13823
      flesch: 47
     summary: Social media disinformation is very widely used as a tool of influence: computational propaganda has been described as a pervasive and ubiquitous part of modern everyday life [8] . Having shared material known to be untrue at the time (Table 6 ) was significantly predicted by lower Agreeableness and lower age.
    keywords: analysis; disinformation; facebook; likelihood; literacy; material; media; participants; people; sharing; stories; studies; study
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        item: #50 of 54
          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
       words: 3167
      flesch: 35
     summary: Twitter use is rising amongst healthcare providers nationally and internationally, including in the field of hematology and oncology. The use and impact of Twitter at medical conferences: Best practices and Twitter etiquette Social medicine: Twitter in healthcare Scientists in the Twitterverse Twitter 101 and beyond: introduction to social media platforms available to practicing hematologist/oncologists Risks and benefits of twitter use by hematologists/oncologists in the era of digital medicine Twitter as a tool for communication and knowledge exchange in academic medicine: a guide for skeptics and novices Trends in twitter use by physicians at the American society of clinical oncology annual meeting Analysis of the use and impact of Twitter during American Society of Clinical Oncology annual meetings from 2011 to 2016: focus on advanced metrics and user trends Social media and the practicing hematologist: Twitter 101 for the busy healthcare provider Tweeting the meeting Leveraging social media for cardio-oncology Using social media to promote academic research: identifying the benefits of twitter for sharing academic work Academics and social networking sites: benefits, Problems and Tensions in Professional Engagement with Online Networking First demonstration of one academic institution's consideration of incorporation of social media scholarship into academic promotion Professionalism in the digital age Social media and physicians' online identity crisis Physicians on Twitter Physician violations of online professionalism and disciplinary actions: a national survey of state medical boards Report of the AMA council on ethical and judicial affairs: professionalism in the use of social media Evaluating unconscious bias: speaker introductions at an international oncology conference Gender differences in publication rates in oncology: looking at the past, present, and future Gender differences in Twitter use and influence among health policy and health services researchers Can tweets predict citations?
    keywords: impact; media; medical; physicians; research; tweets; twitter; use
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        item: #51 of 54
          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
       words: 6686
      flesch: 38
     summary: Another American woman was supported by her friend in translating disaster information. The findings of this study imply that there could be two types of disaster information: a) risk information that refers to the potential effect of the disaster, i.e., emergency alert or warning, and b) action-oriented information that carries instructions for reducing the risk, i.e., an evacuation order and itinerary to be followed.
    keywords: communication; crisis; disaster; ecology; ecosystem; information; japan; japanese; media; resilience; risk; social; study; tweets
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        item: #52 of 54
          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
       words: 9393
      flesch: 45
     summary: Looking closely at the Covid-19 themed crime tweets in particular (Fig. 3) , the majority concerned fraud (57.22%), followed by cybercrime (16.85%), general crime (13.46%) and domestic abuse (12.52%). Covid-19 general crime tweets mostly included warnings about criminals exploiting the pandemic (in general terms) and victimisation.
    keywords: analysis; covid-19; crime; data; e.g.; et al; example; fraud; media; pandemic; police; public; research; stakeholders; tweets; twitter; use
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        item: #53 of 54
          id: cord-349898-nvi8h77t
      author: Dinh, Ly
       title: COVID‐19 pandemic and information diffusion analysis on Twitter
        date: 2020-10-22
       words: 4779
      flesch: 48
     summary: In light of diverse findings on the extent to which SIR models can explain information diffusion on social networks, we examine whether there are similarities in our simulated SIR model (SIRsim), observed SIR model based on actual COVID-19 cases (SIRemp), and observed information cascades on Twitter about the virus (INFOcas). In the context of social networks, information diffusion is formally defined as a process by which a piece of information is passed down from one node to another node through an edge (Gruhl, Guha, Liben-Nowell, & Tomkins, 2004; Guille, Hacid, Favre, & Zighed, 2013) .
    keywords: cascades; cases; covid-19; diffusion; information; model; sir; tweets; virus
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        item: #54 of 54
          id: cord-356353-e6jb0sex
      author: Fourcade, Marion
       title: Loops, ladders and links: the recursivity of social and machine learning
        date: 2020-08-26
       words: 14366
      flesch: 35
     summary: Information Corrupting the cyber-commons: Social media as a tool of autocratic stability Policy paradigms, social learning, and the state: The case of economic policymaking in Britain Perceiving persons and groups The architecture of community: Some new proposals on the social consequences of architectural and planning decisions Exposed: Desire and disobedience in the digital age Simmel, the police form and the limits of democratic policing Posthuman learning: Theories of social learning and socialization have explained how people come to assume behaviors and attitudes in ways not well captured by a focus on internal motivation or conscious deliberation (Miller and Dollard 1941; Bandura 1962; Mauss 1979; Elias 2000) .
    keywords: algorithms; data; human; hunger; instance; interactions; learning systems; life; machine learning; meaning; media; network; new; online; people; platforms; power; practices; process; self; social; society; systems; twitter; users; ways; world
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