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

Abstract

Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we "zoom out" to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors. 1

An Emotion-Aware Multitask Approach to Fake News and Rumor Detection Using Transfer Learning

“…In recent years, our reliance on social media as a source of information has increased multi-fold, bringing along exponential increase in the spread of fake news. To counter this, researchers have proposed various approaches for fake news detection Sheng et al, 2022). However, models trained on one domain are often brittle and vulnerable to incorrect predictions for the samples of another domain (Saikh et al, 2019;Pérez-Rosas et al, 2018).…”