Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning

Abstract

To defend against fake news, researchers have developed various methods based on texts. These methods can be grouped as 1) patternbased methods, which focus on shared patterns among fake news posts rather than the claim itself; and 2) fact-based methods, which retrieve from external sources to verify the claim's veracity without considering patterns. The two groups of methods, which have different preferences of textual clues, actually play complementary roles in detecting fake news. However, few works consider their integration. In this paper, we study the problem of integrating patternand fact-based models into one framework via modeling their preference differences, i.e., making the pattern-and fact-based models focus on respective preferred parts in a post and mitigate interference from non-preferred parts as possible. To this end, we build a Preference-aware Fake News Detection Framework (Pref-FEND), which learns the respective preferences of pattern-and fact-based models for joint detection. We first design a heterogeneous dynamic graph convolutional network to generate the respective preference maps, and then use these maps to guide the joint learning of patternand fact-based models for final prediction. Experiments on two realworld datasets show that Pref-FEND effectively captures model preferences and improves the performance of models based on patterns, facts, or both. CCS CONCEPTS• Information systems → Data mining; • Computing methodologies → Natural language processing.

Memory-Guided Multi-View Multi-Domain Fake News Detection

“…As fake news publishers tend to use inflammatory and emotional expressions to draw reader's attention for a wide dissemination, style [39], [50], [64] and emotion [30], [31], [65] are useful patterns for fake news detection. Some methods [66], [67], [68], [69] exploit existing factual sources to detect fake news.…”

Zoom Out and Observe: News Environment Perception for Fake News Detection

“…retrieved evidence for detection. The knowledge sources can be webpages (Popat et al, 2018;Ma et al, 2019;Vo and Lee, 2021;Wu et al, 2021;Sheng et al, 2021b), knowledge graphs (Cui et al, 2020), online encyclopedias (Thorne et al, 2018;Aly et al, 2021), fact-checking article bases (Augenstein et al, 2019;Shaar et al, 2020), etc. Our NEP starts from a different view, for it "zooms out" to observe the news environment where the post spreads.…”

Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo

“…The methods can be divided into two genres: knowledge-based and appearance-based. (Sheng, Zhang, Cao and Zhong, 2021b) Knowledge-based methods for predicting veracity start by collecting evidence and then applying reasoning, but the sources of evidence are diverse. Comment-based methods employed crowd wisdom for prediction (Ruchansky, Seo and Liu, 2017;Shu et al, 2019a;Tian, Liu, Yang, Lyu, Zhang and Fang, 2020).…”

“…Zhang et al (2021) performed a significant test between real and fake news on Chinese Weibo using a diverse emotion feature set of the contents and comments and showed that the emotion signals statistically correlate to news veracity. The emotional features were then used to improve the performance of text-based fake news detectors (Zhang et al, 2021;Sheng et al, 2021b). Our third research question focuses on the role of these emotional signals: RQ3) How are the emotional signals related to the spread of false news among the domains?…”