Author

Di YouFollow

Faculty Advisor or Committee Member

Kyumin Lee, Advisor

Faculty Advisor or Committee Member

Xiangnan Kong, Reader

Identifier

etd-071119-215650

Abstract

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted by social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this thesis, we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms seven state-of-the-art recommendation models, achieving at least 3~5.3% improvement.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Data Science

Project Type

Thesis

Date Accepted

2019-07-11

Accessibility

Restricted-WPI community only

Subjects

deep learning, fact-checking, graph neural network, recommender system

Available for download on Monday, July 11, 2022

Share

COinS