# Framework para Caracterizar Fake News en Terminos de Emociones

arXiv: https://arxiv.org/abs/2112.07035 · HTML: https://mlsanity.com/paper/2112.07035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [Emotion Cognizance Improves Health Fake News Identification](https://mlsanity.com/paper/1906.10365/) (2019)

## Abstract (arXiv)

Social networks have become one of the main information channels for human beings due to the immediate and social interactivity they offer, allowing in some cases to publish what each user considers relevant. This has brought with it the generation of false news or Fake News, publications that only seek to generate uncertainty, misinformation or skew the opinion of readers. It has been shown that the human being is not capable of fully identifying whether an article is really a fact or a Fake News, due to this it is that models arise that seek to characterize and identify articles based on data mining and machine learning. This article proposes a three-layer framework, the main objective of which is to characterize the emotions present in Fake News and to be a tool for future work that identifies the emotional state and intentional state of the public.