Information Pollution by Social Bots

Social media are vulnerable to deceptive social bots, which can impersonate
humans to amplify misinformation and manipulate opinions. Little is known about
the large-scale consequences of such pollution operations. 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 low-quality information. We discuss insights provided by our analysis, with
a focus on the development of countermeasures to increase the resilience of social media users to manipulation.