Whose Lives Matter? Mass Shootings and Social Media Discourses of Sympathy and Policy, 2012–2014

Zhang, Yini; Shah, Dhavan V.; Foley, Jordan; Abhishek, Aman; Lukito, Josephine; Suk, Jiyoun; Kim, Sang Jeong; Sun, Zhan-Li; Pevehouse, Jon C.; Garlough, Christine

doi:10.1093/jcmc/zmz009

Cited by 59 publications

(35 citation statements)

References 40 publications

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“…Out of 1213 accounts tested, we could not retrieve scores for 26 accounts because 10 were suspended by Twitter, 10 were deleted by the user, and 6 became "protected" accounts. Following Zhang et al (2019), we consider any account with the CAP score 0.25 or above as potentially automated.…”

Section: Bot or Not?mentioning

confidence: 99%

Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter

2020

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In late March of 2020, a new hashtag, #FilmYourHospital, made its first appearance on social media. The hashtag encouraged people to visit local hospitals to take pictures and videos of empty hospitals to help “prove” that the COVID-19 pandemic is an elaborate hoax. Using techniques from Social Network Analysis, this case study examines how this conspiracy theory propagated on Twitter and whether the hashtag virality was aided by the use of automation or coordination among Twitter users. We found that while much of the content came from users with limited reach, the oxygen that fueled this conspiracy in its early days came from a handful of prominent conservative politicians and far right political activists on Twitter. These power users used this hashtag to build awareness about the campaign and to encourage their followers to break quarantine and film what is happening at their local hospitals. After the initial boost by a few prominent accounts, the campaign was mostly sustained by pro-Trump accounts, followed by a secondary wave of propagation outside the U.S. The rise of the #FilmYourHospital conspiracy from a single tweet demonstrates the ongoing challenge of addressing false, viral information during the COVID-19 pandemic. While the spread of misinformation can be potentially mitigated by fact-checking and directing people to credible sources of information from public health agencies, false and misleading claims that are driven by politics and supported by strong convictions and not science are much harder to root out.

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Section: Bot or Not?mentioning

confidence: 99%

Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter

2020

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“…0.25-as used by Zhang et al [10], the accounts, classified as bots by Botometer, would consist of around 55% bots (precision = 0.55) and 45% humans. However, around 71% (recall = .29) of the bots in the population would not be identified.…”

Section: Plos Onementioning

confidence: 99%

“…Some of these descriptive approaches are a combination of classifiers and manual analysis [24]. Even though many new bot detection methods are developed every year-outperforming Botometer in some cases- [15,16,18], we focus on Botometer as it is by far the most used tool in studies analyzing bots on Twitter [12], especially in social science [5,7,10].…”

Section: Introductionmentioning

confidence: 99%

The False positive problem of automatic bot detection in social science research

Rauchfleisch¹,

Kaiser²

2020

PLoS ONE

128

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The identification of bots is an important and complicated task. The bot classifier "Botometer" was successfully introduced as a way to estimate the number of bots in a given list of accounts and, as a consequence, has been frequently used in academic publications. Given its relevance for academic research and our understanding of the presence of automated accounts in any given Twitter discourse, we are interested in Botometer's diagnostic ability over time. To do so, we collected the Botometer scores for five datasets (three verified as bots, two verified as human; n = 4,134) in two languages (English/German) over three months. We show that the Botometer scores are imprecise when it comes to estimating bots; especially in a different language. We further show in an analysis of Botometer scores over time that Botometer's thresholds, even when used very conservatively, are prone to variance, which, in turn, will lead to false negatives (i.e., bots being classified as humans) and false positives (i.e., humans being classified as bots). This has immediate consequences for academic research as most studies in social science using the tool will unknowingly count a high number of human users as bots and vice versa. We conclude our study with a discussion about how computational social scientists should evaluate machine learning systems that are developed for identifying bots.

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“…The last subject category, social media, can be accepted as a part of management subject. During the social media age, the predictions on big data (Niu et al 2017;Saboo et al 2016), social media analyses (Luo and Zhang 2013;Zhang et al 2019), word of mouth (Li Hitt 2008) and election analyses on Twitter (Conway et al 2015) are some of the important research topics.…”

Section: Time Series Analysis Studies In Lismentioning

confidence: 99%

Forecasting the future of library and information science and its sub-fields

Taşkın

2020

Scientometrics

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Forecasting is one of the methods applied in many studies in the library and information science (LIS) field for numerous purposes, from making predictions of the next Nobel laureates to potential technological developments. This study sought to draw a picture for the future of the LIS field and its sub-fields by analysing 97 years of publication and citation patterns. The core Web of Science indexes were used as the data source, and 123,742 articles were examined in-depth for time series analysis. The social network analysis method was used for sub-field classification. The field was divided into four sub-fields: (1) librarianship and law librarianship, (2) health information in LIS, (3) scientometrics and information retrieval and (4) management and information systems. The results of the study show that the LIS sub-fields are completely different from each other in terms of their publication and citation patterns, and all the sub-fields have different dynamics. Furthermore, the number of publications, references and citations will increase significantly in the future. It is expected that more scholars will work together. The future subjects of the LIS field show astonishing diversity from fake news to predatory journals, open government, e-learning and electronic health records. However, the findings prove that publish or perish culture will shape the field. Therefore, it is important to go beyond numbers. It can only be achieved by understanding publication and citation patterns of the field and developing research policies accordingly.

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Whose Lives Matter? Mass Shootings and Social Media Discourses of Sympathy and Policy, 2012–2014

Cited by 59 publications

References 40 publications

Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter

Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter

The False positive problem of automatic bot detection in social science research

Forecasting the future of library and information science and its sub-fields

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