EA2N: Evidence-Based AMR Attention Network for Fake News Detection

Proliferation of fake news has become a critical issue in today's information-driven society. Our study includes external knowledge from Wikidata which allows the model to cross-reference factual claims with established knowledge. This approach deviates from the reliance on social information to detect fake news that many state-of-the-art (SOTA) fact-checking models adopt. This paper introduces EA$^{2}$2N, an Evidence-based AMR (abstract meaning representation) Attention Network for Fake News Detection. EA$^{2}$N utilizes the proposed Evidence based Abstract Meaning Representation (WikiAMR) which incorporates knowledge using a proposed evidence-linking algorithm, pushing the boundaries of fake news detection. The proposed framework encompasses a combination of a novel language encoder and a graph encoder to detect fake news. While the language encoder effectively combines transformer-encoded textual features with affective lexical features, the graph encoder encodes semantic relations with evidence through external knowledge, referred to as WikiAMR graph. A path-aware graph learning module is designed to capture crucial semantic relationships among entities over evidence. Extensive experiments support our model's superior performance, surpassing SOTA methodologies with a difference of 2-3% in F1-score and accuracy for Politifact and Gossipcop datasets. The improvement due to the introduction of WikiAMR is found to be statistically significant with t-value less than 0.01.