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

arXiv: https://arxiv.org/abs/1910.01160 · HTML: https://mlsanity.com/paper/1910.01160/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias](https://mlsanity.com/paper/2304.14133/) (2023)
- [Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline](https://mlsanity.com/paper/2106.03389/) (2021)
- [Towards Detecting Harmful Agendas in News Articles](https://mlsanity.com/paper/2302.00102/) (2023)
- [Variation between Credible and Non-Credible News Across Topics](https://mlsanity.com/paper/2411.12458/) (2024)
- [Towards a New Science of Disinformation](https://mlsanity.com/paper/2204.01489/) (2022)
- [Classification of news spreading barriers](https://mlsanity.com/paper/2304.08167/) (2023)
- [Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English](https://mlsanity.com/paper/2411.10730/) (2024)
- [WISE: Web Information Satire and Fakeness Evaluation](https://mlsanity.com/paper/2512.24000/) (2025)

## Abstract (arXiv)

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.