A Multitask Learning Approach for Fake News Detection: Novelty, Emotion, and Sentiment Lend a Helping Hand

The recent explosion in false information on social media has led to intensive research on automatic fake news detection models and fact-checkers. Fake news and misinformation, due to its peculiarity and rapid dissemination, have posed many interesting challenges to the Natural Language Processing (NLP) and Machine Learning (ML) community. Admissible literature shows that novel information includes the element of surprise, which is the principal characteristic for the amplification and virality of misinformation. Novel and emotional information attracts immediate attention in the reader. Emotion is the presentation of a certain feeling or sentiment. Sentiment helps an individual to convey his emotion through expression and hence the two are co-related. Thus, Novelty of the news item and thereafter detecting the Emotional state and Sentiment of the reader appear to be three key ingredients, tightly coupled with misinformation. In this paper we propose a deep multitask learning model that jointly performs novelty detection, emotion recognition, sentiment prediction, and misinformation detection. Our proposed model achieves the state-of-the-art(SOTA) performance for fake news detection on three benchmark datasets, viz. ByteDance, Fake News Challenge(FNC), and Covid-Stance with 11.55%, 1.58%, and 21.76% improvement in accuracy, respectively. The proposed approach also shows the efficacy over the single-task framework with an accuracy gain of 11.53, 28.62, and 14.31 percentage points for the above three datasets. The source code is available at https://github.com/Nish-19/Multitask-Fake-News-NES.