Update: mining user-news engagement patterns for dual-target cross-domain fake news detection

Abstract

Transfer of knowledge across domains is the focus for cross-domain and multi-domain fake news detection. However, most of the existing methods based on cross-domain knowledge transfer have two issues: (1) they sacrifice domainspecific features; (2) they are less effective in handling the imbalanced data distribution across domains. Targeting these two issues, we focus on how to effectively leverage user-news engagements in both data-richer and data-sparser domains. This is because not only users’ engagement characteristics closely relate to the veracity of the engaged news, but also there are consistent patterns in common users’ engagements with news across domains. Considering these two insights, this work aims to perform dual-target cross-domain fake news detection via well modeling users’ engagement patterns. In particular, it aims to transfer knowledge based on user-news engagements for handling the imbalanced data distribution across domains, which is novel but challenging. To this end, in this paper, we propose a novel framework to mine User-news engagement Patterns for DuAl-TargEt cross-domain fake news detection (UPDATE). In UPDATE, we consider common users from different domains and mine user-news engagement patterns as the key auxiliary information for cross-domain knowledge transfer. In such a way, it avoids the necessity to remove the domain-specific news information, and thereby, better preserve useful information. Then, we extract users’ engagement features in each domain and combine the features of common users from different domains to obtain more user information. By doing so, UPDATE improves the information richness in each of the two domains, thus improving detection performance in both domains when detecting news from domains with imbalanced data distribution. Extensive experiments conducted on real-world datasets demonstrate that UPDATE significantly outperforms state-of-the-art cross-domain and multi-domain methods as well as large language models (LLMs), such as GPT-3.5-turbo in terms of AUC and F1-score for fake news detection.