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

TL;DR

This paper introduces a semi-supervised fake news detection approach that leverages sentiment encoding, LSTM, and self-attention, achieving improved accuracy with limited labeled data on social media datasets.

Contribution

It presents a novel semi-supervised learning framework combining sentiment analysis with LSTM and self-attention for fake news detection, reducing reliance on large labeled datasets.

Findings

Outperforms existing methods in precision and recall

Effective with limited labeled data

Benchmark results on 20,000 news items

Abstract

Micro-blogs and cyber-space social networks are the main communication mediums to receive and share news nowadays. As a side effect, however, the networks can disseminate fake news that harms individuals and the society. Several methods have been developed to detect fake news, but the majority require large sets of manually labeled data to attain the application-level accuracy. Due to the strict privacy policies, the required data are often inaccessible or limited to some specific topics. On the other side, quite diverse and abundant unlabeled data on social media suggests 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 in a semi-supervised fashion and incorporates LSTM with self-attention layers. We benchmark our model on a dataset with 20,000 news content along with their feedback, which shows better performance in precision, recall, and measures compared to competitive methods in fake news detection.