What the fake? Probing misinformation detection standing on the shoulder of novelty and emotion. Issue 1 (January 2022) - Kumari, Rina; Ashok, Nischal; Ghosal, Tirthankar; Ekbal, Asif
Abstract: One of the most time-critical challenges for the Natural Language Processing (NLP) community is to combat the spread of fake news and misinformation. Existing approaches for misinformation detection use neural network models, statistical methods, linguistic traits, fact-checking strategies, etc. However, the menace of fake news seems to grow more vigorous with the advent of humongous and unusually creative language models. Relevant literature reveals that one major characteristic of the virality of fake news is the presence of an element of surprise in the story, which attracts immediate attention and invokes strong emotional stimulus in the reader. In this work, we leverage this idea and propose textual novelty detection and emotion prediction as the two tasks relating to automatic misinformation detection. We re-purpose textual entailment for novelty detection and use the models trained on large-scale datasets of entailment and emotion to classify fake information. Our results correlate with the idea as we achieve state-of-the-art (SOTA) performance (7.92%, 1.54%, 17.31% and 8.13% improvement in terms of accuracy) on four large-scale misinformation datasets. We hope that our current probe will motivate the community to explore further research on misinformation detection along this line. The source code is available at the GitHub. 2 Highlights: "Novelty" is a key attribute for misinformation or fake news. False stories inspired negative emotions and true stories inspired positive emotions. We explore the role of novelty and emotion to classify misinformation content. We use pre-trained models on Textual Novelty Detection and Emotion Prediction tasks. We design a transfer learning-based deep neural architecture for fake news detection.