Information Pollution by Social Bots Xiaodan Lou Alessandro Flammini Filippo Menczer arXiv:1907.06130v1 [cs.CY] 13 Jul 2019 Center for Complex Networks and Systems Research Indiana University, Bloomington Abstract Social media are vulnerable to deceptive...
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Information Pollution by Social Bots Xiaodan Lou Alessandro Flammini Filippo Menczer arXiv:1907.06130v1 [cs.CY] 13 Jul 2019 Center for Complex Networks and Systems Research Indiana University, Bloomington Abstract Social media are vulnerable to deceptive social bots, which can im- personate humans to amplify misinformation and manipulate opinions. Little is known about the large-scale consequences of such pollution oper- ations. Here we introduce an agent-based model of information spreading with quality preference and limited individual attention to evaluate the impact of different strategies that bots can exploit to pollute the network and degrade the overall quality of the information ecosystem. We find that penetrating a critical fraction of the network is more important than generating attention-grabbing content and that targeting random users is more damaging than targeting hub nodes. The model is able to reproduce empirical patterns about exposure amplification and virality of l
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