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Adaptive Rumor Suppression on Social Networks: A Multi-Round Hybrid Approach

Published: 11 January 2025 Publication History

Abstract

Rumor suppression is targeted at diminishing the impact of false and negative information within social networks by decreasing the prevalence of belief in such rumors among individuals, utilizing diverse strategies. Previous studies have broadly delineated rumor suppression strategies into two primary categories: targeting key nodes or edges for obstruction, and enlisting high-influence nodes to disseminate truth-related accurate information. Traditionally, employing a singular strategy involves utilizing a static algorithm throughout the rumor suppression endeavor. This method, however, encounters difficulties in adapting to fluctuating external conditions, rendering it less efficacious in the management of rumor proliferation. In response to these challenges, we introduce the concept of Adaptive Rumor Suppression (ARS), which aims to dynamically counter rumors by taking into account the nuances of propagation dynamics and the surrounding environmental context. We propose a multi-label state transition linear threshold model to more closely mirror the complex process of information diffusion across social networks. Furthermore, we advocate for a multi-round hybrid strategy that amalgamates blocking and clarification tactics to address the ARS problem within the confines of limited resource allocations. To navigate the complexities of ARS, we introduce the Hybrid Strategy of Each Round (HS-R) algorithm, which synergizes multiple strategies to effectively counter the spread of rumors. In extension, we present the Multi-Round Multi-Label (MRML) algorithm, designed to augment the efficiency of the HS-R algorithm. Experimental evaluations conducted on authentic social network datasets illustrate that our methodologies significantly outshine baseline algorithms, offering a more effective and adaptable solution to curb rumor propagation across varied environments.

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  1. Adaptive Rumor Suppression on Social Networks: A Multi-Round Hybrid Approach

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        Published In

        cover image ACM Transactions on Knowledge Discovery from Data
        ACM Transactions on Knowledge Discovery from Data  Volume 19, Issue 2
        February 2025
        127 pages
        EISSN:1556-472X
        DOI:10.1145/3703012
        • Editor:
        • Jian Pei
        Issue’s Table of Contents

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 11 January 2025
        Online AM: 31 October 2024
        Accepted: 17 October 2024
        Revised: 15 September 2024
        Received: 02 April 2024
        Published in TKDD Volume 19, Issue 2

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        Author Tags

        1. Social networks
        2. Rumor suppression
        3. Multi-round hybrid strategy
        4. Propagation model Associate Editor: Lingyang Chu

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        • Research-article

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        • National Key R&D Program of China
        • National Natural Science Foundation of China
        • Shanghai Rising-Star Program
        • Fundamental Research Funds for the Central Universities

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