XFake: Explainable Fake News Detector with Visualizations

Shiva Kumar Pentyala

Abstract:

we present the XFake system, an explainable
fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items,
we jointly consider both attributes (e.g., speaker) and statements.
Specifically, MIMIC, ATTN and PERT frameworks are designed,
where MIMIC is built for attribute analysis, ATTN is for statement
semantic analysis and PERT is for statement linguistic analysis.
Beyond the explanations extracted from the designed frameworks,
relevant supporting examples as well as visualization are further
provided to facilitate the interpretation. Our implemented system
is demonstrated on a real-world dataset crawled from PolitiFact1,
where thousands of verified political news have been collected.