Variation between Credible and Non-Credible News Across Topics

TL;DR

This paper analyzes how linguistic features of fake news vary across different topics, emphasizing the need for topic-specific adaptation in deception detection to improve classification accuracy.

Contribution

It introduces a linguistic and stylistic analysis across five news topics, highlighting topic-dependent variations in fake news features for better detection.

Findings

Linguistic features differ significantly between credible and fake news across topics.

Topic-specific adaptation improves fake news classification performance.

Variation in stylistic features emphasizes the need for tailored detection models.

Abstract

'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.