DARIM: Dynamic Approach for Rumor Influence Minimization in Online Social Networks

Adil Imad Eddine Hosni, Kan Li*, Sadique Ahmad

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

This paper investigates the problem of rumor influence minimization in online social networks (OSNs). Over the years, researchers have proposed strategies to diminish the influence of rumor mainly divided into two well-known methods, namely the anti-rumor campaign strategy and the blocking nodes strategy. Although these strategies have proven to be efficient in different scenarios, their gaps remain in other situations. Therefore, we introduce in this work the dynamic approach for rumor influence minimization (DARIM) that aims to overcome these shortcomings and exploit their advantage. The objective is to find a compromise between the blocking nodes and anti-rumor campaign strategies that minimize the most the influence of a rumor. Accordingly, we present a solution formulated from the perspective of a network inference problem by exploiting the survival theory. Thus, we introduce a greedy algorithm based on the likelihood principle. Since the problem is NP-hard, we prove the objective function is submodular and monotone and provide an approximation within $$(1-1/\textit{e})$$ of the optimal solution. Experiments performed in real multiplex and single OSNs provide evidence about the performance of the proposed algorithm compared the work of literature.

Original languageEnglish
Title of host publicationNeural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
EditorsTom Gedeon, Kok Wai Wong, Minho Lee
PublisherSpringer
Pages619-630
Number of pages12
ISBN (Print)9783030367107
DOIs
Publication statusPublished - 2019
Event26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia
Duration: 12 Dec 201915 Dec 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11954 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Neural Information Processing, ICONIP 2019
Country/TerritoryAustralia
CitySydney
Period12/12/1915/12/19

Keywords

  • Anti-rumor campaign strategy
  • Blocking nodes strategy
  • Rumor influence minimization
  • Rumor propagation

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