"Attention" for Detecting Unreliable News in the Information Age

TL;DR

This paper develops and evaluates hierarchical attention network models to automatically detect unreliable news articles, achieving high accuracy and providing interpretability through attention visualization.

Contribution

It introduces variants of hierarchical attention networks for unreliable news detection and demonstrates their effectiveness on a real dataset.

Findings

Achieved 0.944 ROC-AUC in detecting unreliable news.

Hierarchical attention networks outperform other NLP models.

Attention weights offer insights into decision-making processes.

Abstract

An Unreliable news is any piece of information which is false or misleading, deliberately spread to promote political, ideological and financial agendas. Recently the problem of unreliable news has got a lot of attention as the number instances of using news and social media outlets for propaganda have increased rapidly. This poses a serious threat to society, which calls for technology to automatically and reliably identify unreliable news sources. This paper is an effort made in this direction to build systems for detecting unreliable news articles. In this paper, various NLP algorithms were built and evaluated on Unreliable News Data 2017 dataset. Variants of hierarchical attention networks (HAN) are presented for encoding and classifying news articles which achieve the best results of 0.944 ROC-AUC. Finally, Attention layer weights are visualized to understand and give insight into the decisions made by HANs. The results obtained are very promising and encouraging to deploy and use these systems in the real world to mitigate the problem of unreliable news.