Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior

Luceri, Luca; Deb, Ashok; Badawy, Adam; Ferrara, Emilio

doi:10.1145/3308560.3316735

Cited by 88 publications

(55 citation statements)

References 29 publications

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“…As mentioned in the previous section, Luceri et al (2019) observed that conservative bots might be more confusing for everyone, but they also noticed that human-bot connections and interactions happened more frequently between conservative bots and conservative users. We thus want to explore whether conservative users might be more biased when recognizing bots from conservative profiles.…”

Section: Participantsmentioning

confidence: 61%

Asymmetrical perceptions of partisan political bots

Yan

Yang

Menczer

et al. 2020

New Media & Society

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Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study investigates the ability to differentiate bots with partisan personas from humans on Twitter. Our online experiment ( N = 656) explores how various characteristics of the participants and of the stimulus profiles bias recognition accuracy. The analysis reveals asymmetrical partisan-motivated reasoning, in that conservative profiles appear to be more confusing and Republican participants perform less well in the recognition task. Moreover, Republican users are more likely to confuse conservative bots with humans, whereas Democratic users are more likely to confuse conservative human users with bots. We discuss implications for how partisan identities affect motivated reasoning and how political bots exacerbate political polarization.

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Section: Participantsmentioning

confidence: 61%

Asymmetrical perceptions of partisan political bots

Yan

Yang

Menczer

et al. 2020

New Media & Society

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“…In this study, we followed previous research [ 23 , 68 , 69 ] and chose Twitter to explore the sentiment features of social bots during the COVID-19 pandemic. According to the COVID-19 pandemic timeline revealed by the WHO [ 70 ], we selected three important time points relating to the WHO’s reactions as data collection timestamps.…”

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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“…Since then, service providers have been increasing their efforts to suspend malicious actors and maintain a healthy conversation on their platforms. However, nefarious activity on social media has not entirely stopped: social media bots (in short, bots ), automated and software-controlled accounts (Ferrara et al ., 2016a), and trolls, human operators often associated with foreign entities, are still active online ( Badawy et al , 2018a;Luceri et al , 2019;Im et al , 2019). These play a pivotal role in information and disinformation campaigns globally (Ratkiewicz et al , 2011;Metaxas and Mustafaraj, 2012;Ferrara, 2017;Howard et al , 2017;Shu et al , 2017;Badawy et al , 2018b;Vosoughi et al , 2018;Stella et al , 2018;Bovet and Makse, 2019;Gringberg et al , 2019;Scheufele & Krause, 2019;Stella et al , 2019, Ruck et al , 2019.…”

Section: Introductionmentioning

confidence: 99%

Evolution of bot and human behavior during elections

Luceri¹,

Deb²,

Giordano³

et al. 2019

Self Cite

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Online social media have become one of the main communication platforms for political discussion. The online ecosystem, however, does not only include human users but has given a space to an increasing number of automated accounts, referred to as bots, extensively used to spread messages and manipulate the narratives others are exposed to. Although social media service providers put increasing efforts to protect their platforms, malicious bot accounts continuously evolve to escape detection. In this work, we monitored the activity of almost 245K accounts engaged in the Twitter political discussion during the last two U.S. voting events. We identified approximately 31K bots and characterized their activity in contrast with humans. We show that, in the 2018 midterms, bots changed the volume and the temporal dynamics of their online activity to better mimic humans and avoid detection. Our findings highlight the mutable nature of bots and illustrate the challenges to forecast their evolution.

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Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior

Cited by 88 publications

References 29 publications

Asymmetrical perceptions of partisan political bots

Asymmetrical perceptions of partisan political bots

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

Evolution of bot and human behavior during elections

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