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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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Understanding the Effects of Large Language Model (LLM)-driven Adversarial Social Influences in Online Information Spread
Misinformation on social media poses significant societal challenges, particularly with the rise of large language models (LLMs) that can amplify its realism and reach. This study examines how adversarial social influence generated by LLM-powered bots affects people’s online information processing. Via a pre-registered, randomized human-subject experiment, we examined the effects of two types of LLM-driven adversarial influence: bots posting comments contrary to the news veracity and bots replying adversarially to human comments. Results show that both forms of influence significantly reduce participants’ ability to detect misinformation and discern true news from false. Additionally, adversarial comments were more effective than replies in discouraging the sharing of real news. The impact of these influences was moderated by political alignment, with participants more susceptible when the news conflicted with their political leanings. Guided by these findings, we conclude by discussing the targeted interventions to combat misinformation spread by adversarial social influences.
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- Award ID(s):
- 2229876
- PAR ID:
- 10663424
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3706599.3720019
