An Emotional Analysis of False Information in Social Media and News Articles
This addresses the problem of fake news detection for social media users and platforms, but it is incremental as it builds on existing emotional analysis methods.
The study analyzed emotional patterns in false versus real news across types like propaganda and satire, finding distinct emotional signatures that aid deception. They developed an emotionally-infused LSTM model for false news detection, though no specific performance metrics were provided.
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.