Characterizing the roles of bots on Twitter during the COVID-19 infodemic

Xu, Wentao; Sasahara, Kazutoshi

doi:10.1007/s42001-021-00139-3

Cited by 20 publications

(26 citation statements)

References 28 publications

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“…Our study shows that social bots were used to criticize and harass political opponents rather than to provide useful information on health measures and self-protection behavior in a context where quality information was sorely needed in the face of widespread misinformation [ 47 ]. In-line with our results, a recent study indicates that right-wing self-media accounts and conspiracy theorists may give rise to this opinion polarization, whereas malicious bots may foster the diffusion of noncredible information [ 42 ]. We have not found large amounts of misinformation on health issues but rather major divisions regarding political decision-making processes and the measures to address the COVID-19 pandemic (eg, vaccines and protective measures, etc).…”

Section: Discussionsupporting

confidence: 90%

“…Although the percentage of undeclared bots on Twitter is relatively low compared to the large number of human users, it has been established that bots are generally linked to web-based conversations characterized by controversy and polarization. In this sense, the role of these automatic agents is far from negligible, considering the role they play in the amplification of ideas and opinions that generate conflict in our societies [ 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

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Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism

Suárez-Lledó¹,

Álvarez‐Gálvez²

2022

J Med Internet Res

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Background Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation. Objective This study aimed to study the role of social media bots during the COVID-19 outbreak. Methods The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet. Results Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively. Conclusions By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism

Suárez-Lledó¹,

Álvarez‐Gálvez²

2022

J Med Internet Res

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“…After some unsuccessful experiments in classifying bots using traditional methods due to the restrictions limiting how much of the total content published by a user could be collected (only related to COVID-19), we chose to use Botometer 3 (Shao et al. 2018 ; Xu and Sasahara 2021 ; Sayyadiharikandeh et al. 2020 ; Yang et al.…”

Section: Related Workmentioning

confidence: 99%

Political polarization on Twitter during the COVID-19 pandemic: a case study in Brazil

Brum

Teixeira

Vimieiro

et al. 2022

Soc. Netw. Anal. Min.

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The debate over the COVID-19 pandemic is constantly trending at online conversations since its beginning in 2019. The discussions in many social media platforms is related not only to health aspects of the disease, but also public policies and non-pharmacological measures to mitigate the spreading of the virus and propose alternative treatments. Divergent opinions regarding these measures are leading to heated discussions and polarization. Particularly in highly politically polarized countries, users tend to be divided in those in-favor or against government policies. In this work we present a computational method to analyze Twitter data and: (i) identify users with a high probability of being bots using only COVID-19 related messages; (ii) quantify the political polarization of the Brazilian general public in the context of the COVID-19 pandemic; (iii) analyze how bots tweet and affect political polarization. We collected over 100 million tweets from 26 April 2020 to 3 January 2021, and observed in general a highly polarized population (with polarization index varying from 0.57 to 0.86), which focuses on very different topics of discussions over the most polarized weeks–but all related to government and health-related events.

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“…The impeachment and handling of COVID-19 have received particular attention on Twitter ( Dejard et al, 2021 ; Cervi, García & Marín-Lladó, 2021 ), as key moments of his presidency. While the use of bots to create social media content during presidential elections and COVID-19 have received attention ( Xu & Sasahara, 2021 ), there is still a need to examine how bots were used during the first impeachment.…”

Section: Introductionmentioning

confidence: 99%

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

Galgoczy

Phatak

Vinson

et al. 2022

PeerJ Computer Science

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Influencing and framing debates on Twitter provides power to shape public opinion. Bots have become essential tools of ‘computational propaganda’ on social media such as Twitter, often contributing to a large fraction of the tweets regarding political events such as elections. Although analyses have been conducted regarding the first impeachment of former president Donald Trump, they have been focused on either a manual examination of relatively few tweets to emphasize rhetoric, or the use of Natural Language Processing (NLP) of a much larger corpus with respect to common metrics such as sentiment. In this paper, we complement existing analyses by examining the role of bots in the first impeachment with respect to three questions as follows. (Q1) Are bots actively involved in the debate? (Q2) Do bots target one political affiliation more than another? (Q3) Which sources are used by bots to support their arguments? Our methods start with collecting over 13M tweets on six key dates, from October 6th 2019 to January 21st 2020. We used machine learning to evaluate the sentiment of the tweets (via BERT) and whether it originates from a bot. We then examined these sentiments with respect to a balanced sample of Democrats and Republicans directly relevant to the impeachment, such as House Speaker Nancy Pelosi, senator Mitch McConnell, and (then former Vice President) Joe Biden. The content of posts from bots was further analyzed with respect to the sources used (with bias ratings from AllSides and Ad Fontes) and themes. Our first finding is that bots have played a significant role in contributing to the overall negative tone of the debate (Q1). Bots were targeting Democrats more than Republicans (Q2), as evidenced both by a difference in ratio (bots had more negative-to-positive tweets on Democrats than Republicans) and in composition (use of derogatory nicknames). Finally, the sources provided by bots were almost twice as likely to be from the right than the left, with a noticeable use of hyper-partisan right and most extreme right sources (Q3). Bots were thus purposely used to promote a misleading version of events. Overall, this suggests an intentional use of bots as part of a strategy, thus providing further confirmation that computational propaganda is involved in defining political events in the United States. As any empirical analysis, our work has several limitations. For example, Trump’s rhetoric on Twitter has previously been characterized by an overly negative tone, thus tweets detected as negative may be echoing his message rather than acting against him. Previous works show that this possibility is limited, and its existence would only strengthen our conclusions. As our analysis is based on NLP, we focus on processing a large volume of tweets rather than manually reading all of them, thus future studies may complement our approach by using qualitative methods to assess the specific arguments used by bots.

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Characterizing the roles of bots on Twitter during the COVID-19 infodemic

Cited by 20 publications

References 28 publications

Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism

Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism

Political polarization on Twitter during the COVID-19 pandemic: a case study in Brazil

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

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