Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks

Schuchard, Ross; Crooks, Andrew; Stefanidis, Anthony; Croitoru, Arie

doi:10.1007/978-3-030-05414-4_34

Cited by 17 publications

(22 citation statements)

References 21 publications

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“…Moreover, human users retweet bots at a higher rate than bots retweet humans in each of the conversations, which demonstrates that humans are more responsible for spreading bot-generated content than bots themselves in each of the mass shooting conversations. This is an interesting finding, as previous social bot analysis has found bots to be more, on average, hyper-social than humans: they attempt to engage humans at persistently higher rates in retweet networks associated with election, conflict, and political Twitter conversations as opposed to average human accounts (Stella et al, 2018;Schuchard et al, 2019).…”

Section: Resultsmentioning

confidence: 72%

“…It is necessary to assert that the automated behaviour of bots does not imply intentional malice, as many bots serve in benign roles (e.g., news feed aggregator) (Ferrara et al, 2016). Further introductory works have analysed the presence of social bots in various polarising OSN conversations such as elections (Howard and Kollanyi, 2016;Bessi and Ferrara, 2016), conflict (Schuchard et al, 2019) and vaccinations (Subrahmanian et al, 2016;Broniatowski et al, 2018).…”

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confidence: 99%

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Bots fired: examining social bot evidence in online mass shooting conversations

et al. 2019

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Mass shootings, like other extreme events, have long garnered public curiosity and, in turn, significant media coverage. The media framing, or topic focus, of mass shooting events typically evolves over time from details of the actual shooting to discussions of potential policy changes (e.g., gun control, mental health). Such media coverage has been historically provided through traditional media sources such as print, television, and radio, but the advent of online social networks (OSNs) has introduced a new platform for accessing, producing, and distributing information about such extreme events. The ease and convenience of OSN usage for information within society's larger growing reliance upon digital technologies introduces potential unforeseen risks. Social bots, or automated software agents, are one such risk, as they can serve to amplify or distort potential narratives associated with extreme events such as mass shootings. In this paper, we seek to determine the prevalence and relative importance of social bots participating in OSN conversations following mass shooting events using an ensemble of quantitative techniques. Specifically, we examine a corpus of more than 46 million tweets produced by 11.7 million unique Twitter accounts within OSN conversations discussing four major mass shooting events: the 2017 Las Vegas concert shooting, the 2017 Sutherland Springs church chooting, the 2018 Parkland School Shooting and the 2018 Santa Fe school shooting. This study's results show that social bots participate in and contribute to online mass shooting conversations in a manner that is distinguishable from human contributions. Furthermore, while social bots accounted for fewer than 1% of total corpus user contributors, social network analysis centrality measures identified many bots with significant prominence in the conversation networks, densely occupying many of the highest eigenvector and out-degree centrality measure rankings, to include 82% of the top-100 eigenvector values of the Las Vegas retweet network.

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Section: Resultsmentioning

confidence: 72%

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confidence: 99%

Bots fired: examining social bot evidence in online mass shooting conversations

et al. 2019

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“…In an earlier paper (Schuchard et al 2019) we presented a study that focused on developing an initial framework to characterize the pervasiveness and relative importance of social bots in OSNs. This current paper extends this earlier work by providing a more robust contribution along three lines of effort.…”

Section: Introductionmentioning

confidence: 99%

Bot stamina: examining the influence and staying power of bots in online social networks

et al. 2019

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This study presents a novel approach to expand the emergent area of social bot research. We employ a methodological framework that aggregates and fuses data from multiple global Twitter conversations with an available bot detection platform and ultimately classifies the relative importance and persistence of social bots in online social networks (OSNs). In testing this methodology across three major global event OSN conversations in 2016, we confirmed the hyper-social nature of bots: suspected social bot accounts make far more attempts on average than social media accounts attributed to human users to initiate contact with other accounts via retweets. Social network analysis centrality measurements discover that social bots, while comprising less than 0.3% of the total corpus user population, display a disproportionately high level of structural network influence by ranking particularly high among the top users across multiple centrality measures within the OSN conversations of interest. Further, we show that social bots exhibit temporal persistence in centrality ranking density when examining these same OSN conversations over time.

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“…Information retrieval was traditionally defined as "finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers) [106]". Computational social scientists engage in this activity by collecting and analyzing any digital traces that potentially address their social science inquiries (such as elections and international relations, e.g., [107][108][109][110]). There are technical challenges in this realm that include evaluation of item similarities, data scalability, and time sensitivity [111].…”

Section: Information Retrieval and Open Data Systemsmentioning

confidence: 99%

Computational Social Science of Disasters: Opportunities and Challenges

Burger

Kennedy

et al. 2019

Future Internet

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Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

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Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks

Cited by 17 publications

References 21 publications

Bots fired: examining social bot evidence in online mass shooting conversations

Bots fired: examining social bot evidence in online mass shooting conversations

Bot stamina: examining the influence and staying power of bots in online social networks

Computational Social Science of Disasters: Opportunities and Challenges

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