Fake News in Social Networks
Type
working paper
Author(s)
Abstract (De)
We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while
remaining flexible tomodel re-specification. Optimized strategies allow agents to correctly identify most false claims,when all agents receive unbiased private signals. However, an adversary’s attempt to spread fake news by targeting a subset of agents with a biased private
signal can be successful. Even more so when the adversary has information about agents’network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary’s attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users’ private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.
remaining flexible tomodel re-specification. Optimized strategies allow agents to correctly identify most false claims,when all agents receive unbiased private signals. However, an adversary’s attempt to spread fake news by targeting a subset of agents with a biased private
signal can be successful. Even more so when the adversary has information about agents’network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary’s attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users’ private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.
Language
English
Keywords
social learning
networks
multi-agent deep reinforcement learning
HSG Classification
contribution to scientific community
Publisher
SoF-HSG
Official URL
Subject(s)
Division(s)
Eprints ID
253524
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18_04_Aymanns et al_Fake News in Social Networks.pdf
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