Browsing by Subject "Misinformation propagation"

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Open Access
Misinformation propagation in online social networks: game theoretic and reinforcement learning approaches
(IEEE, 2022-09-30) Yılmaz, Tolga; Ulusoy, Özgür
Misinformation in online social networks (OSNs) has been an ongoing problem, and it has been studied heavily over recent years. In this article, we use gamification to tackle misinformation propagation in OSNs. First, we construct a game based on the notion of cooperative games on graphs where the nodes of the social network are players. We use random regular networks and real networks in our simulations to show that the constructed game follows evolutionary dynamics and that the outcome of the game depends on the relation between the structural properties of the network and the benefit and cost variables defined in a cooperative game. Second, we create a game on the network level where the players control a set of nodes. We define agents whose goal is to maximize the total reward that we set up to be the number of nodes affected at the end of the game. We propose a deep reinforcement learning (RL) technique based on the multiagent deep deterministic policy gradient (MADDPG) algorithm. We test the proposed method along with well-known node selection algorithms and obtain promising results on different social networks.
Open Access
Towards modeling and mitigating misinformation propagation in online social networks
(2023-01) Yılmaz, Tolga
Misinformation on the internet and social media has become a pressing concern due to its potential impacts on society, undermining trust and impacting human decisions on global issues such as health, energy, politics, terrorism, and disasters. As a solution to the problem, computational methods have been employed to detect and mitigate the spread of false or misleading information. These efforts have included the development of algorithms to identify fake news and troll accounts, as well as research on the dissemination of misinformation on social media platforms. However, the problem of misinformation on the web and social networks remains a complex and ongoing challenge, requiring continued attention and research. We contribute to three different solution aspects of the problem. First, we design and implement an extensible social network simulation framework called Crowd that helps model, simulate, visualize and analyze social network scenarios. Second, we gamify misinformation propagation as a cooperative game between nodes and identify how misinformation spreads under various criteria. Then, we design a network-level game where the nodes are controlled from a higher perspective. In this game, we train and test a deep reinforcement learning method based on Multi-Agent Deep Deterministic Policy Gradients and show that our method outperforms well-known node-selection algorithms, such as page-rank, centrality, and CELF, over various social networks in defending against misinformation or participating in it. Finally, we promote and propose a blockchain and deep learning hybrid approach that utilizes crowdsourcing to target the misinformation problem while providing transparency, immutability, and validity of votes. We provide the results of extensive simulations under various combinations of well-known attacks on reputation systems and a case study that compares our results with a current study on Twitter.