A Hybrid Computer-Intensive Approach Integrating Machine Learning and Statistical Methods for Fake News Detection-Reference-Cited by-同舟云学术

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A Hybrid Computer-Intensive Approach Integrating Machine Learning and Statistical Methods for Fake News Detection

Published:2024-07-16 Issue: Volume:7 Page:47
ISSN:
Container-title:ITISE 2024
language:
Short-container-title:

Author:

Fenga Livio¹^ORCID

Affiliation:

1. Center for Simulation Analysis and Modelling, Faculty of Science, Innovation, Technology, and Entrepreneurship, University of Exeter, Exeter EX4 4PU, UK

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/68/1/47/pdf

Reference16 articles.

1. The spread of misinformation in the 2016 U.S. presidential election;Nyhan;Res. Politics,2020

2. Fake News Detection on Social Media: A Data Mining Perspective;Ahmed;ACM Sigkdd Explor. Newsl.,2017

3. The spread of true and false news online;Vosoughi;Science,2018

4. Shearer, E., Grieco, E., Walker, M., and Mitchell, A. (2024, March 03). News Use Across Social Media Platforms 2018. Pew Research Center. Available online: https://www.journalism.org/2018/09/10/news-use-across-social-media-platforms-2018/.

5. Pérez-Rosas, V., Kleinberg, B., Lefevre, A., and Mihalcea, R. (2018, January 20–26). Automatic detection of fake news. Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, New Mexico.

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