2024/06/28 by Bao Tran Truong, Xiaodan Lou, Alessandro Flammini +1 · 2 voices · 16 citations
Computer Science · Mathematics · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Adversarial system #Artificial intelligence #Business #Computer science #Computer security #Internet privacy #Law #Mathematics #Network Security and Intrusion Detection #Political science #Public defender #Public relations #Square (algebra)
paper · doi:10.1093/pnasnexus/pgae258
published in PNAS Nexus 3(7), pgae258 (Oxford University Press)
openalex publication_date 2024/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.