Abstract
Easier access to the Internet and social media has made disseminating information through online sources very easy. Sources like Facebook, Twitter, online news sites and blogs of self-proclaimed journalists have become significant players in providing news content. The sheer amount of information and the speed at which it is generated online makes it beyond the scope of human verification. There is, hence, a pressing need to develop technologies that can assist humans with automatic fact-checking and reliable identification of fake news. This paper summarises the multiple approaches that were undertaken and the experiments that were carried out for the task. Credibility information and metadata associated with the news article have been used for improved results. The experiments also show how modelling justification or evidence can lead to improved results. Additionally, the use of visual features in addition to linguistic features is demonstrated. A detailed comparison of the results showing that our models perform significantly well when compared to robust baselines, and state-of-the-art models are presented.
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Bhatt, S., Goenka, N., Kalra, S., Sharma, Y. (2022). Fake News Detection: Experiments and Approaches Beyond Linguistic Features. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-16-2937-2_9
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DOI: https://doi.org/10.1007/978-981-16-2937-2_9
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