An Emotional Analysis of False Information in Social Media and News Articles

TL;DR

This paper analyzes emotional patterns in false news across different types and proposes an emotionally-infused LSTM model for detecting misinformation in social media and news articles.

Contribution

It introduces a comparative emotional analysis of false versus real news and develops a novel LSTM model incorporating emotional cues for false news detection.

Findings

False news exhibits distinct emotional patterns by type.

Emotional cues significantly aid in identifying false information.

The proposed model outperforms baseline detection methods.

Abstract

Fake news is risky since it has been created to manipulate the readers' opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news articles sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed a LSTM neural network model that is emotionally-infused to detect false news.