Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues

TL;DR

This paper develops a machine learning approach that combines semantic representations and linguistic cues to distinguish fake news from satire, addressing the challenge of automatic classification amidst subtle language nuances.

Contribution

It introduces a novel method integrating semantic and linguistic features for improved fake news versus satire classification, outperforming baseline language models.

Findings

Semantic and linguistic cues improve classification accuracy.

The approach outperforms language-based baseline models.

Nuances between fake news and satire are effectively identified.

Abstract

The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.