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Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., et al. (2018) EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, 19-23 August 2018, 849-857.
https://doi.org/10.1145/3219819.3219903

has been cited by the following article:

• TITLE: A Review of Methods for Detecting Rumors on Social Networks Using Social Circle Mining

  AUTHORS: Alaa Alassaf, Delel Rhouma

  KEYWORDS: Rumor Detection, Social Network Analysis, Social Circles, Graph Mining

  JOURNAL NAME: Social Networking, Vol.14 No.2, April 30, 2025

  ABSTRACT: Currently, social platforms provide places for people not only to gather information but also to generate and propagate rumors. Consequently, rumor detection has become a major global task for mining fake information from social networks. Although social circles have the capacity to describe users’ behavior preferences and to impact the scope and spreading speed of rumors, numerous studies have ignored them when designing rumor prediction models. To address this oversight, we conducted a technical investigation and comparison of state-of-the-art procedures for detecting rumors, focusing on the role of data mining in social circles. This survey will assist researchers in determining the most effective techniques and appropriate future research directions.