# Emotion Cognizance Improves Health Fake News Identification

arXiv: https://arxiv.org/abs/1906.10365 · HTML: https://mlsanity.com/paper/1906.10365/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias](https://mlsanity.com/paper/2204.10365/) (2022)
- [Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation](https://mlsanity.com/paper/2211.13718/) (2022)
- [Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023](https://mlsanity.com/paper/2309.07925/) (2023)
- [HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability](https://mlsanity.com/paper/2407.00668/) (2024)
- [Framework para Caracterizar Fake News en Terminos de Emociones](https://mlsanity.com/paper/2112.07035/) (2021)
- [An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning](https://mlsanity.com/paper/2211.12374/) (2022)
- [REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection](https://mlsanity.com/paper/2301.08995/) (2023)
- [Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences](https://mlsanity.com/paper/2507.03488/) (2025)

## Abstract (arXiv)

Identifying misinformation is increasingly being recognized as an important computational task with high potential social impact. Misinformation and fake contents are injected into almost every domain of news including politics, health, science, business, etc., among which, the fakeness in health domain pose serious adverse effects to scare or harm the society. Misinformation contains scientific claims or content from social media exaggerated with strong emotion content to attract eyeballs. In this paper, we consider the utility of the affective character of news articles for fake news identification in the health domain and present evidence that emotion cognizant representations are significantly more suited for the task. We outline a technique to leverage emotion intensity lexicons to develop emotionized text representations, and evaluate the utility of such a representation for identifying fake news relating to health in various supervised and unsupervised scenarios. The consistent and significant empirical gains that we observe over a range of technique types and parameter settings establish the utility of the emotional information in news articles, an often overlooked aspect, for the task of misinformation identification in the health domain.