Fake News in Social Networks
Christoph Aymanns (),
Jakob Foerster and
No 1804, Working Papers on Finance from University of St. Gallen, School of Finance
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 to model re-specification. Optimized strategies allow agents to correctly identifymostfalseclaims, whenallagentsreceiveunbiasedprivatesignals. However, anadversary’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.
Keywords: social learning; networks; multi-agent deep reinforcement learning (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-gth, nep-mic, nep-net, nep-soc and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:usg:sfwpfi:2018:04
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