Learning Automata-based Misinformation Mitigation via Hawkes Processes

Ahmed Abouzeid (), Ole-Christoffer Granmo (), Christian Webersik () and Morten Goodwin ()
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Ahmed Abouzeid: University of Agder
Ole-Christoffer Granmo: University of Agder
Christian Webersik: University of Agder
Morten Goodwin: University of Agder

Information Systems Frontiers, 2021, vol. 23, issue 5, No 7, 1169-1188

Abstract: Abstract Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint random walk over the state space. We use three Twitter datasets to evaluate our approach, one of them being a new COVID-19 dataset provided in this paper. Our approach shows fast convergence and increased valid information exposure. These results persisted independently of network structure, including networks with central nodes, where the latter could be the root of misinformation. Further, the LA obtained these results in a decentralized manner, facilitating distributed deployment in real-life scenarios.

Keywords: Learning automata; Stochastic optimization; Social media Misinformation; Crisis mitigation; Hawkes processes (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-020-10102-8

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