Year: 2025
Type: article
Abstract: In the digital age, social networks have become critical platforms for information dissemination, but they also pose significant risks due to the rapid spread of rumors and misinformation. Existing approaches to rumor control often rely on models that assume a single exposure to anti-rumor information is sufficient to mitigate its impact, overlooking the necessity of multiple impressions for effective behavior change. In this work, we address the Rumor Control with Impression Counts problem by proposing the first-ever Machine Learning (ML)-based solution. Our approach leverages Graph Neural Networks combined with a greedy algorithm to efficiently manage large-scale social networks. To further enhance computational efficiency, we incorporate Evolutionary Optimization, resulting in a method that not only addresses the effectiveness challenges but also scales efficiently with network size. Extensive experiments on real-world datasets demonstrate that our approach outperforms existing methods in both effectiveness and scalability, improving computational efficiency by 1 to 2 orders of magnitude.
Source: Data Science and Engineering
Authors: Pengfei Xu, Liwei Wang, Zhiyong Peng
Institution: Wuhan University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,390
Award: G1002713634
OpenAlex ID: W4413435648