Multiple Q-DQN Algorithm based Rumor Control in Social Networks

Zainab Hasan, Computer Science, University of Babylon, Hilla, IraqFollow
Huda Naji Nawaf, Computer Science, University of Babylon, Hilla, IraqFollow

ORCID

Zainab Hasan: https://orcid.org/0000-0001-5918-7576

Article Type

Original Study

Abstract

Malicious rumours on social media platforms like Facebook, Twitter, and others can be widely disseminated because of social problems. It is challenging to manage a rumour once it gains growth and quickly moves throughout a network. One of the main issues with information dissemination is figuring out how to reduce the propagation of rumours within a social network. A deep reinforcement learning technique might be an effective strategy to manage the rumour issue. Deep Q learning network (DQN) has been used in the literature to mitigate rumors by selecting a blocker at each time step. In this work, the proposed Multiple-Q-DQN is introduced as a stepwise discrete-time optimization method for rumor control to address the rumor influence minimization problem. The Multiple-Q-DQN model helps identify and select two blocker nodes at each time step while considering the changing state of the network. This implies that rather than using a static technique that is ineffective in light of changes, the strategy might take into account the network's present status. Additionally, sequential decision-making—a methodical optimisation process—is employed. The infection rate and comparisons with baseline and competitive methods are used to analyse the model's performance using four real-world datasets, including American football, Facebook, Zachary Karate Club, and Dolphins. The Multiple-Q-DQN is superior to the baseline in terms of infection rate at the first time step on the Facebook network (0.09:0.10), Karate network (0.03:0.07), and Dolphins network (0.02:0.09), except for the Football network (0.09:0.09). Also, all networks are superior to all related works, while the Facebook network is competitive with related works.

Keywords

Rumor control, Reinforcement learning, Deep Q-learning, Rumor diffusion models

How to Cite This Article

Hasan, Zainab and Nawaf, Huda Naji (2026) "Multiple Q-DQN Algorithm based Rumor Control in Social Networks," Journal of Intelligent Informatics, Networking, and Cybersecurity: Vol. 2 : Iss. 1 , Article 1.
Available at: https://doi.org/10.65445/3106-1192.1007