Investigating the Validity of Botometer-Based Social Bot Studies

Gallwitz, Florian; Kreil, Michael

doi:10.1007/978-3-031-18253-2_5

Cited by 14 publications

(10 citation statements)

References 13 publications

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“…The top 50 accounts in each community with the highest CAP were chosen after computing the CAP score for all accounts, all of which were in the highly probable class. Botometer’s result is uncertain and typically bears levels of error (Gallwitz & Kreil, 2022; B. Zhao et al, 2023).…”

Section: Methodsmentioning

confidence: 99%

Bots Versus Humans: Discursive Activism During the Pandemic in the Iranian Twittersphere

Kermani,

Bayat Makou,

Tafreshi

et al. 2023

Social Media + Society

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This article explores how the pandemic in Iran was discursively framed by automated accounts and human users. While there is a growing body of literature on bot activism, little is known about how bots and humans framed the pandemic in authoritarian regimes. Drawing on networked framing theory, we use both computational and qualitative methods to fill this gap. Our empirical analysis centers on a data set of 4,165,177 tweets collected between 27 January 2020 and 18 April 2020. We found that while anti-regime human users strongly criticized Iran’s regime, pro-regime bots countered with messages emphasizing the sacrifices of medical staff, the strength of Iran, and the failings of Western governments in managing the crisis. Our results suggest that Persian Twitter human users were largely against the regime, while the regime employed bots extensively to maintain balance. Human users used sarcasm, while pro-regime bots invoked religious and revolutionary sentiments metaphorically to defend the regime. By focusing on a relatively unexplored context, this article adds to the growing literature on bot activism.

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Section: Methodsmentioning

confidence: 99%

Bots Versus Humans: Discursive Activism During the Pandemic in the Iranian Twittersphere

Kermani,

Bayat Makou,

Tafreshi

et al. 2023

Social Media + Society

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“…Political bots rely on algorithms and automated scripts, disseminating misleading information purposefully by imitating the behavior of real people to guide or participate in political events (Woolley & Howard, 2016). Political bots played a strategic role in political events, for example, COVID-19 (Robles et al, 2022), the Brexit referendum (Howard & Kollanyi, 2016), political elections (Bessi & Ferrara, 2016;Ferrara, 2017;Vogt, 2012), climate change (Gallwitz & Kreil, 2022) and so on. More specifically, Woolley and Howard (2017) found that political bots would constantly integrate information and quickly produce content to influence votes or slander critics.…”

Section: Social Botmentioning

confidence: 99%

“…Studies on the mechanism of detection tools such as Debot (Chavoshi et al, 2016), SocialBotHunter (Dorri et al, 2018), and Botometer (Sayyadiharikandeh et al, 2020) have emerged. However, Gallwitz and Kreil (2022) pointed out that the current research about the prevalence or influence of social bots might be misleading, possibly caused by flawed bot detection methods. Therefore, relevant research needs to strictly examine the IDs of social bots to ensure authenticity.…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of social bots in information warfare: A case study on @UAWeapons Twitter account in the context of Russia–Ukraine conflict

Liu

et al. 2023

Communication and the Public

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During the Russia–Ukraine conflict, social media has become an outlet for public opinion; therefore, besides the hot war, information warfare is also taking place. It was discovered that a large number of new social bot accounts had emerged on Twitter since the outbreak of the conflict. In particular, the Twitter account @UAWeapons has grown its following by hundreds of thousands in fewer than 30 days and has established itself as an influential opinion leader. Through data mining and three different analysis methods, this study investigated how social bots grew to influence public opinion. Specifically, time-series analysis revealed an unusual pattern of “pulsing” changes in the number of tweets posted by @UAWeapons over time. The content analysis illustrated that the account posted biased tweets in favor of Ukraine under the guise of being a neutral messenger, which led to frequent retweets from social bots with similar opinions. Finally, the results of the social network analysis indicated that @UAWeapons’ rapid growth might be attributed to a strategy of network clustering implemented by a core group of social bot accounts.

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“…These often masquerade as genuine users that are hard for ordinary users to identify as bots (Ferrara et al, 2016; Hwang et al, 2012). Since many bots do not explicitly reveal their nature or intent, classification remains a challenge (Davis et al, 2016; Van Der Walt & Eloff, 2018; Wang et al, 2012), with most approaches relying on probabilistic and predictive models (Ferrara et al, 2016; Wojcik et al, 2018), to varying degrees of success and accuracy (Gallwitz & Kreil, 2022; Rauchfleisch & Kaiser, 2020; Torusdağ et al, 2020). The problem is exacerbated by the adoption of inconsistent conceptual and operational criteria for bots detection (Martini et al, 2021).…”

Section: Social Bots As An Emergent Entrant In Online Ecosystemmentioning

confidence: 99%

Not All Bots are Created Equal: The Impact of Bots Classification Techniques on Identification of Discursive Behaviors Around the COVID-19 Vaccine and Climate Change

Wang

Walter

Ophir

2023

Social Science Computer Review

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As concerns about social bots online increase, studies have attempted to explore the discourse they produce, and its effects on individuals and the public at large. We argue that the common reliance on aggregated scores of binary classifiers for bot detection may have yielded biased or inaccurate results. To test this possibility, we systematically compare the differences between non-bots and bots using binary and non-binary classifiers (classified into the categories of astroturf, self-declared, spammers, fake followers, and Other). We use two Twitter corpora, about COVID-19 vaccines ( N = 1,697,280) and climate change ( N = 1,062,522). We find that both in terms of volume and thematic content, the use of binary classifiers may hinder, distort, or mask differences between humans and bots, that could only be discerned when observing specific bot types. We discuss the theoretical and practical implications of these findings.

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Investigating the Validity of Botometer-Based Social Bot Studies

Cited by 14 publications

References 13 publications

Bots Versus Humans: Discursive Activism During the Pandemic in the Iranian Twittersphere

Bots Versus Humans: Discursive Activism During the Pandemic in the Iranian Twittersphere

Influence of social bots in information warfare: A case study on @UAWeapons Twitter account in the context of Russia–Ukraine conflict

Not All Bots are Created Equal: The Impact of Bots Classification Techniques on Identification of Discursive Behaviors Around the COVID-19 Vaccine and Climate Change

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