# Variation between Credible and Non-Credible News Across Topics

arXiv: https://arxiv.org/abs/2411.12458 · HTML: https://mlsanity.com/paper/2411.12458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (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)
- [What to do about non-standard (or non-canonical) language in NLP](https://mlsanity.com/paper/1608.07836/) (2016)
- [Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues](https://mlsanity.com/paper/1910.01160/) (2019)
- [Studying Dishonest Intentions in Brazilian Portuguese Texts](https://mlsanity.com/paper/2008.06079/) (2020)

## Built on by (most cited first, 1 of 1)

- [Are LLMs More Skeptical of Entertainment News?](https://mlsanity.com/paper/2605.01727/) (2026)

## Abstract (arXiv)

'Fake News' continues to undermine trust in modern journalism and politics. Despite continued efforts to study fake news, results have been conflicting. Previous attempts to analyse and combat fake news have largely focused on distinguishing fake news from truth, or differentiating between its various sub-types (such as propaganda, satire, misinformation, etc.) This paper conducts a linguistic and stylistic analysis of fake news, focusing on variation between various news topics. It builds on related work identifying features from discourse and linguistics in deception detection by analysing five distinct news topics: Economy, Entertainment, Health, Science, and Sports. The results emphasize that linguistic features vary between credible and deceptive news in each domain and highlight the importance of adapting classification tasks to accommodate variety-based stylistic and linguistic differences in order to achieve better real-world performance.