Interpretable fake news detection with topic and deep variational models

Author:

Hosseini MarjanORCID,Javadian Sabet AlirezaORCID,He SuiningORCID,Aguiar DerekORCID

Funder

University of Connecticut

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Communication,Information Systems

Reference77 articles.

1. The science of fake news;Lazer;Science,2018

2. Fake News. it’s Complicated;Wardle,2017

3. A short guide to the history of’fake news’ and disinformation;Posetti;Int. Cent. Journal.,2018

4. The echo chamber effect on social media;Cinelli;Proc. Natl. Acad. Sci.,2021

5. M. Chalkiadakis, A. Kornilakis, P. Papadopoulos, E. Markatos, N. Kourtellis, The Rise and Fall of Fake News sites: A Traffic Analysis, in: 13th ACM Web Science Conference 2021, 2021, pp. 168–177.

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1. A Framework for Enhancing Social Media Misinformation Detection with Topical-Tactics;Digital Threats: Research and Practice;2024-06-09

2. Analyzing Network Characteristics for Misinformation Detection in Online Social Media;2024 4th International Conference on Data Engineering and Communication Systems (ICDECS);2024-03-22

3. TransVAE-PAM: A Combined Transformer and DAG-based Approach for Enhanced Fake News Detection in Indian Context;ACM Transactions on Asian and Low-Resource Language Information Processing;2024-03-02

4. A Knowledge-Based Deep Learning Approach for Automatic Fake News Detection using BERT on Twitter;Procedia Computer Science;2024

5. The Impact of Trendy Topics on Information Diffusion;Proceedings of the 2023 12th International Conference on Software and Information Engineering;2023-11-21

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