FakEDAMR: Fake News Detection Using Abstract Meaning Representation Network

Given the rising prevalence of disinformation and fake news online, the detection of fake news in social media posts has become an essential task in the field of social network analysis and NLP. In this paper, we propose a fake detection model named, FakEDAMR that encodes textual content using the Abstract Meaning Representation (AMR) graph, a semantic representation of natural language that captures the underlying meaning of a sentence. The graphical representation of textual content holds longer relation dependency in very few distances. A new fake news dataset, FauxNSA, has been created using tweets from the Twitter platform related to ‘Nupur Sharma’ and ‘Agniveer’ political controversy. We embed each sentence of the tweet using an AMR graph and then use this in combination with textual features to classify fake news. Experimental results on publicly and proposed datasets with two different sets show that adding AMR graph features improves F1-score and accuracy significantly. (Code and Dataset: https://github.com/shubhamgpt007/FakedAMR)