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.

Creator

• You, Di

Contributors

• Kong, Xiangnan
• Lee, Kyumin

Degree

• MS

Unit

• Data Science

Publisher

• Worcester Polytechnic Institute

Identifier

• etd-071119-215650

Keyword

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

Advisor

• Lee, Kyumin

Committee

• Kong, Xiangnan

Defense date

• 2019-06-06

Year

• 2019

Date created

• 2019-07-11

Resource type

• Thesis

Rights statement

• In Copyright

Last modified

• 2021-01-05