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

Shi, Wen; Liu, Diyi; Yang, Jing; Zhang, Jing; Wen, Sanmei; Su, Jing

doi:10.3390/ijerph17228701

Cited by 62 publications

(58 citation statements)

References 86 publications

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“…Here we have shown that a majority of known bots are tweeting about COVID-19, a finding that corroborates similar studies [ 68 , 70 ]. Early in the pandemic, one study found that 45% of COVID-19related tweets originate from bots [ 71 ], although Twitter has pushed back on this claim, citing false-positive detection algorithms [ 72 ].…”

Section: Implications For the Covid-19 Pandemicsupporting

confidence: 92%

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Himelein-Wachowiak¹,

Giorgi²,

Devoto³

et al. 2021

J Med Internet Res

125

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As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms—a so-called “infodemic.” In this viewpoint, we survey past literature investigating the role of automated accounts, or “bots,” in spreading such misinformation, drawing connections to the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.

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Section: Implications For the Covid-19 Pandemicsupporting

confidence: 92%

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Himelein-Wachowiak¹,

Giorgi²,

Devoto³

et al. 2021

J Med Internet Res

125

View full text Add to dashboard Cite

show abstract

“…Here we have shown that a majority of known bots are tweeting about COVID-19, a finding that corroborates similar studies [68,70]. Early in the pandemic, one study found that 45% of COVID-19-related tweets originate from bots [71], although Twitter has pushed back on this claim, citing false-positive detection algorithms [72].…”

Section: Implications For the Covid-19 Pandemicsupporting

confidence: 89%

Bots and Misinformation Spread on Social Media: Implications for COVID-19 (Preprint)

Himelein-Wachowiak¹,

Giorgi²,

Devoto³

et al. 2021

Preprint

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UNSTRUCTURED As of December 2020, the SARS-CoV-2 virus has been responsible for over 78 million cases of COVID-19 worldwide, resulting in over 1.7 million deaths. In the United States in particular, protective measures against the COVID-19 pandemic have been hampered by political polarization and discrepancies among federal, state, and local policies. As a result, a huge amount of information surrounding COVID-19, some of it contradictory or blatantly false, has proliferated on social media. In this mixed scoping review, we survey the role of automated accounts, or “bots,” in spreading misinformation during past epidemics, natural disasters, and politically polarizing events through the lens of the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and machine learning methods for detecting bot activity. We conclude by conducting and presenting a secondary analysis of known bots, finding that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.

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“…It is also possible that nonhuman Twitter users (bots) were represented in our sample. Previous research has found that Twitter bots have manipulated public opinion and fueled cascades of negative emotions related to topics about COVID-19 [ 34 ]. Without any way to systematically identify and exclude these tweets, we suspect that several such tweets were included in our analysis.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework

Griffith¹,

Marani²,

Monkman³

2021

J Med Internet Res

189

176

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Background With the approval of two COVID-19 vaccines in Canada, many people feel a sense of relief, as hope is on the horizon. However, only about 75% of people in Canada plan to receive one of the vaccines. Objective The purpose of this study is to determine the reasons why people in Canada feel hesitant toward receiving a COVID-19 vaccine. Methods We screened 3915 tweets from public Twitter profiles in Canada by using the search words “vaccine” and “COVID.” The tweets that met the inclusion criteria (ie, those about COVID-19 vaccine hesitancy) were coded via content analysis. Codes were then organized into themes and interpreted by using the Theoretical Domains Framework. Results Overall, 605 tweets were identified as those about COVID-19 vaccine hesitancy. Vaccine hesitancy stemmed from the following themes: concerns over safety, suspicion about political or economic forces driving the COVID-19 pandemic or vaccine development, a lack of knowledge about the vaccine, antivaccine or confusing messages from authority figures, and a lack of legal liability from vaccine companies. This study also examined mistrust toward the medical industry not due to hesitancy, but due to the legacy of communities marginalized by health care institutions. These themes were categorized into the following five Theoretical Domains Framework constructs: knowledge, beliefs about consequences, environmental context and resources, social influence, and emotion. Conclusions With the World Health Organization stating that one of the worst threats to global health is vaccine hesitancy, it is important to have a comprehensive understanding of the reasons behind this reluctance. By using a behavioral science framework, this study adds to the emerging knowledge about vaccine hesitancy in relation to COVID-19 vaccines by analyzing public discourse in tweets in real time. Health care leaders and clinicians may use this knowledge to develop public health interventions that are responsive to the concerns of people who are hesitant to receive vaccines.

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

Cited by 62 publications

References 86 publications

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Bots and Misinformation Spread on Social Media: Implications for COVID-19 (Preprint)

COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework

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