International Conference on Computer and Knowledge Engineering

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A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention

Authors :

Pouya Shaeri¹ Ali Katanforoush²

1- Shahid Beheshti University 2- Shahid Beheshti University

Keywords :

Fake News Detection،Semi-supervised،Fake News،Misinformation،Disinformation

Abstract :

Micro-blogs and cyber-space social networks are the main communication medium to receive and share news, nowadays. As a side effect, however, the networks can disseminate the fake news that harm individuals and society, too. Several methods have been developed to detect fake news, but the most require large sets of manually labeled data to attain to an application-level accuracy. Due to the strict privacy policies, the required data are often inaccessible or limited to some specific topics which do not cover the features needed for a comprehensive analysis of news. On the other side, quite diverse and abundant unlabeled data on social media suggest that with a few labeled data the problem of detecting fake news could be tackled via semi-supervised learning. Here, we propose a semi-supervised self-learning method in which a sentiment analysis is acquired by some state-of-the-art pretrained models. Our learning model is trained through a semi-supervised fashion and incorporates LSTM with self-attention layers. We benchmark our model on a dataset with 20’000 news content with their feedback that shows better performance in precision, recall and measures compared to competitive methods in fake news detection.

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