Fake News Detection using Multilingual Evidence

Published in PhD Thesis, 2020

Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. As a result, it becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose the new technique based on multilingual evidence that can be used for fake news detection and improve existing approaches. This approach imporved baseline systems for fake news detection and added more explainability for the users. Below you can examine graphical abstract of this work.

Figure. The approach containes the follwong steps: 1) Text Extraction from the new coming article. 2) Text Translation into several languages. 3) Cross-lingual News Retrieval based on translated text. 4) Content Similarity Computation between the retrieved articles and the original one. 5) News Classification into true if there is enough evidence, or fake if there is contradiction.