On End-to-end Automatic Fact-checking Systems

The emergence of social media has aided the spread of nonfactual information across the internet, and organizations are combating disinformation by performing manual fact-checking. Due to the massive amount of online information, the automation of this process has recently gained great interest. Previous works have formulated several automatic fact-checking tasks, and explored machine learning and natural language processing approaches to the problems. In this thesis we follow this line of work, aim to build a fully-working automatic fact-checking system, and study methods for improving its fact-checking abilities. First, we introduce an end-to-end automatic fact-checking framework that integrates multiple previously studied subtasks to predict the factuality of given claims while providing supporting evidence. Next we explore the use of multi-task learning for improving factuality predictions. Finally, we devise methods for extracting temporal structure from news documents to aid the fact-checking process.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Fang, Wei

Advisor dc:contributor.advisor

• Glass, James R.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139967

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139967