ArticleProceedings of the National Academy of Sciences of the United States of America2021
Bots are less central than verified accounts during contentious political events.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Quantifying Bot Impact: An Information-Theoretic Analysis of Complexity and Uncertainty in Online Political Communication Dynamics.Entropy (Basel, Switzerland) · 2025Article
- Modeling public attention behavior on badminton's ecosystem evolution via complex network theory.Frontiers in sports and active living · 2025Article
- Social media users' attitudes toward cyberbullying during the COVID-19 pandemic: associations with gender and verification status.Frontiers in psychology · 2024Article
- Who supports Bernie? Analyzing identity and ideological variation on Twitter during the 2020 democratic primaries.PloS one · 2024Article
- Exposure to social bots amplifies perceptual biases and regulation propensity.Scientific reports · 2023Article
- The systemic impact of deplatforming on social media.PNAS nexus · 2023Article
- Negative expressions are shared more on Twitter for public figures than for ordinary users.PNAS nexus · 2023Article
- Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
- The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing.Royal Society open science · 2022Article
- Bow-tie structures of twitter discursive communities.Scientific reports · 2022Article
- The advantage of the right in social media news sharing.PNAS nexus · 2022Article
- Do Twitter users change their behavior after exposure to misinformation? An in-depth analysis.Social network analysis and mining · 2022Article
- Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election.EPJ data science · 2022Article
- Comparative analysis of social bots and humans during the COVID-19 pandemic.Journal of computational social science · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information manipulation is widespread in today's media environment. Online networks have disrupted the gatekeeping role of traditional media by allowing various actors to influence the public agenda; they have also allowed automated accounts (or bots) to blend with human activity in the flow of information. Here, we assess the impact that bots had on the dissemination of content during two contentious political events that evolved in real time on social media. We focus on events of heightened political tension because they are particularly susceptible to information campaigns designed to mislead or exacerbate conflict. We compare the visibility of bots with human accounts, verified accounts, and mainstream news outlets. Our analyses combine millions of posts from a popular microblogging platform with web-tracking data collected from two different countries and timeframes. We employ tools from network science, natural language processing, and machine learning to analyze the diffusion structure, the content of the messages diffused, and the actors behind those messages as the political events unfolded. We show that verified accounts are significantly more visible than unverified bots in the coverage of the events but also that bots attract more attention than human accounts. Our findings highlight that social media and the web are very different news ecosystems in terms of prevalent news sources and that both humans and bots contribute to generate discrepancy in news visibility with their activity.
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