Fake News Detection in Benchmark Dataset using Hybrid Deep Neural Model

Akshi Kumar, Prerna Patel

The anonymity veil and virality related to the social platforms have been tactfully used to deceive and mislead society through creating or disseminating fake news. The content and meta-data associated with the information posted can serve as evidence to debunk its truth value. A hybrid deep neural model is proposed which combines a high-level representation of textual features learned using hierarchical attention network and meta-data features learnt using convolutional neural network for the multi-label classification of fake news in benchmark LIAR dataset. The experiments on this model-level fusion network achieves a performance accuracy of 43.8% and outperforms the existing state-of-the-art.

Volume 12 | Issue 6

Pages: 2132-2145

DOI: 10.5373/JARDCS/V12I6/S20201175