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With the ease of access to information, and its rapid dissemination over theinternet (both velocity and volume), it has become challenging to filter outtruthful information from fake ones. The research community is now faced withthe task of automatic detection of fake news, which carries real-worldsocio-political impact. One such research contribution came in the form of theConstraint@AAA12021 Shared Task on COVID19 Fake News Detection in English. Inthis paper, we shed light on a novel method we proposed as a part of thisshared task. Our team introduced an approach to combine topical distributionsfrom Latent Dirichlet Allocation (LDA) with contextualized representations fromXLNet. We also compared our method with existing baselines to show that XLNet +Topic Distributions outperforms other approaches by attaining an F1-score of0.967.

Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task