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

arXiv: https://arxiv.org/abs/1908.09951 · HTML: https://mlsanity.com/paper/1908.09951/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Using Persuasive Writing Strategies to Explain and Detect Health Misinformation](https://mlsanity.com/paper/2211.05985/) (2022)
- [Zero and Few-shot Learning for Author Profiling](https://mlsanity.com/paper/2204.10543/) (2022)
- [Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021](https://mlsanity.com/paper/2207.05133/) (2022)
- [The Rise and Fall of Fake News sites: A Traffic Analysis](https://mlsanity.com/paper/2103.09258/) (2021)
- [IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification](https://mlsanity.com/paper/2208.14001/) (2022)
- [Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection](https://mlsanity.com/paper/2407.12880/) (2024)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)
- [Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims](https://mlsanity.com/paper/2108.03731/) (2021)
- [Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020](https://mlsanity.com/paper/2207.11893/) (2022)
- [Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media](https://mlsanity.com/paper/2508.16223/) (2025)

## Abstract (arXiv)

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.