Variation between Credible and Non-Credible News Across Topics
This work addresses the issue of fake news undermining trust in journalism and politics, but it is incremental as it builds on existing deception detection methods by focusing on topic-specific variations.
The paper tackled the problem of fake news by analyzing linguistic and stylistic differences between credible and deceptive news across five topics (Economy, Entertainment, Health, Science, Sports), finding that these features vary by domain and that adapting classification to such differences improves real-world performance.
'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.