Evolution of bot and human behavior during elections

Luceri, Luca; Deb, Ashok; Giordano, Silvia; Ferrara, Emilio

doi:10.5210/fm.v24i9.10213

Cited by 40 publications

(42 citation statements)

References 33 publications

(44 reference statements)

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“…Given that social bots have become increasingly sophisticated, the lines between human-like and bot-like behaviors have become further blurred [ 19 ]. Researchers have developed multiple approaches to discriminate between social bots and humans [ 71 , 72 ], among which Botometer , formerly known as BotOrNot , is a widely used social bot detection interface for Twitter [ 73 , 74 , 75 , 76 ].…”

Section: Methodsmentioning

confidence: 99%

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Shi

Liu

Yang

et al. 2020

IJERPH

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During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

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Section: Methodsmentioning

confidence: 99%

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Shi

Liu

Yang

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…It is noteworthy that the creators of Botometer will generally speak of “social bots” in their academic work [ 3 , 6 , 12 , 25 ], but have not used the term in the tool’s FAQ system until recently. In Luceri et al [ 26 ], one of the original Botometer creators even understands bot as short for “social media bot”, adding to the confusion. While there is agreement on the definition of “bot” (automation as a deciding factor), the social in “social bot” is contested.…”

Section: Introductionmentioning

confidence: 99%

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

Rauchfleisch¹,

Kaiser²

2020

PLoS ONE

127

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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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“…By affecting the very fabric of our socio-technical systems, these problems are intrinsically interdisciplinary and require joint efforts to investigate and address both the technical (e.g., how to thwart automated accounts and the spread of lowquality information, how to develop algorithms for detecting deception, automation, and manipulation), as well as the socio-cultural aspects (e.g., why do people believe in and share false news, how do interference campaigns evolve over time) [7,15]. Fortunately, in the case of COVID-19, several open datasets were promptly made available to foster research on the aforementioned matters [1,2,6,16].…”

Section: Introductionmentioning

confidence: 99%

Misinformation, manipulation, and abuse on social media in the era of COVID-19

2020

Self Cite

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The COVID-19 pandemic represented an unprecedented setting for the spread of online misinformation, manipulation, and abuse, with the potential to cause dramatic real-world consequences. The aim of this special issue was to collect contributions investigating issues such as the emergence of infodemics, misinformation, conspiracy theories, automation, and online harassment on the onset of the coronavirus outbreak. Articles in this collection adopt a diverse range of methods and techniques, and focus on the study of the narratives that fueled conspiracy theories, on the diffusion patterns of COVID-19 misinformation, on the global news sentiment, on hate speech and social bot interference, and on multimodal Chinese propaganda. The diversity of the methodological and scientific approaches undertaken in the aforementioned articles demonstrates the interdisciplinarity of these issues. In turn, these crucial endeavors might anticipate a growing trend of studies where diverse theories, models, and techniques will be combined to tackle the different aspects of online misinformation, manipulation, and abuse.

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Evolution of bot and human behavior during elections

Cited by 40 publications

References 33 publications

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

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

Misinformation, manipulation, and abuse on social media in the era of COVID-19

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