Online conflict resolution: Algorithm design and analysis

• Published in: Information sciences Vol. 651; p. 119718
• Main Authors: Rao, Guoyao, Li, Deying, Wang, Yongcai, Chen, Wenping, Zhou, Chunlai, Zhu, Yuqing
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2023
• Subjects: Influence maximization; Online conflicts; Social networks; Influence maximization; Social networks; Online conflicts
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2023.119718

In today's digital age, the widespread dissemination of information through social media platforms has a profound impact on people's thoughts and actions. It fosters lively discussions and debates among individuals, but also sparks online conflicts that exacerbate social tensions and divisions. Therefore, it brings a challenge: how to eliminate these online conflicts. In this paper, we analyze and solve it from an algorithmic perspective. We introduce individuals who are willing to persuade their close friends to avoid conflicts with others. Our objective is to strategically select a group of influencers who can encourage others to act as mediators, thus mitigating online conflicts within the realm of social networks. Hence, an optimization problem is formulated, which is proved to be NP-hard and #P-hard. Subsequently, to solve the problem, an algorithm grounded in a reverse sampling technique is proposed, which achieves a performance bound of (1−1/e). Finally, the result of experiments on real-world networks datasets to evaluate our algorithm and shows that our algorithm outperforms all comparisons.