Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic

Weng, Zixuan; Lin, Aiming

doi:10.3390/ijerph192416376

Cited by 17 publications

(8 citation statements)

References 87 publications

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“…While other research has emphasized approaches such as text analysis and network analysis 40 – 42 to analyze user behavior in discussion networks, our study distinguishes itself by employing RMT to gain a deeper understanding of information propagation within this medium. This innovative method sheds light on the dynamics at play and, enhances our grasp of network dynamics in social media discussions.…”

Section: Discussionmentioning

confidence: 99%

A structural approach to detecting opinion leaders in Twitter by random matrix theory

Mohammadi,

Moradi,

Trufanov

et al. 2023

Sci Rep

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This paper presents a novel approach leveraging Random Matrix Theory (RMT) to identify influential users and uncover the underlying dynamics within social media discourse networks. Focusing on the retweet network associated with the 2021 Iranian presidential election, our study reveals intriguing findings. RMT analysis unveils that power dynamics within both poles of the network do not conform to a “one-to-many” pattern, highlighting a select group of users wielding significant influence within their clusters and across the entire network. By harnessing Random Matrix Theory (RMT) and complementary methodologies, we gain a profound understanding of the network’s structure and, in turn, unveil the intricate dynamics of the discussion extending beyond mere structural analysis. In sum, our findings underscore the potential of RMT as a tool to gain deeper insights into network dynamics, particularly within popular discussions. This approach holds promise for investigating opinion leaders in diverse political and non-political dialogues.

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Section: Discussionmentioning

confidence: 99%

A structural approach to detecting opinion leaders in Twitter by random matrix theory

Mohammadi,

Moradi,

Trufanov

et al. 2023

Sci Rep

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“…While there is little research on bot activism during the pandemic in Iran, a significant number of studies on this topic in other contexts are plagued by the same issue. (Cai et al, 2023; Shi et al, 2020; Weng & Lin, 2022; Zhang et al, 2022). Our findings, as discussed below, are an attempt to fill these gaps to some extent.…”

Section: Discussionmentioning

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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“…205 [254] Rising tides or rising stars? : Dynamics of shared attention on twitter during media events 206 [255] Misleading health-related information promoted through video-based social media: Anorexia on youtube 209 [258] Utilising online eye-tracking to discern the impacts of cultural backgrounds on fake and real news decision-making 210 [259] Top 100 #PCOS influencers: Understanding who, why and how online content for PCOS is influenced 211 [260] Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis 214 [263] The influence of fake news on face-trait learning 215 [264] COVID-Related Misinformation Migration to BitChute and Odysee 216 [265] Sending News Back Home: Misinformation Lost in Transnational Social Networks 217 [266] Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic 218 [267] Organization and evolution of the UK far-right network on Telegram 219 [268] Predictive modeling for suspicious content identification on Twitter 220 [269] Detection and moderation of detrimental content on social media platforms: current status and future directions 221 [270] Cross-platform information spread during the January 6th capitol riots 222 [271] Combating multimodal fake news on social media: methods, datasets, and future perspective 223 [272] In. Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat 249 [298] The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic 250 [299] Twelve tips to make successful medical infographics 251 [300] TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets 252 [301] Cognitive and affective responses to political disinformation in Facebook 253 [302] Experience: Managing misinformation in social media-insights for policymakers from Twitter analytics 254 [303] Hepatitis E vaccine in China: Public health professional perspectives on vaccine promotion and strategies for control (Continued )…”

Section: Id Document Referencementioning

confidence: 99%

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

Tomassi,

Falegnami,

Romano

2024

PLoS ONE

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This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

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Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic

Cited by 17 publications

References 87 publications

A structural approach to detecting opinion leaders in Twitter by random matrix theory

A structural approach to detecting opinion leaders in Twitter by random matrix theory

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

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

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