Classifying Real and Bot Users Based on their News Spread for Combating Misinformation
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
Proceedings - 17th IEEE International Conference on Service-Oriented System Engineering, SOSE 2023
DOI
10.1109/SOSE58276.2023.00038
First Page
272
Last Page
279
Abstract
Online social media (OSM) have become the primary global news source, but because of the distributed nature of the web, it has increased the risk of misinformation spread. Fake news is misinformation that masquerades as genuine (real) information. Consequently, it is an active topic for researchers and OSM companies to find ways to identify and flag fake news, as well as detect the responsible sources that generate them. Bots, artificial users designed for various purposes, contribute to the dissemination of information on OSM. Regrettably, bots exhibit faster propagation of information compared to real users, often leading to the spread of inaccurate and low-quality information [1]. Differentiating between bots and real users solely based only on content poses a formidable task. This paper explores and expands techniques for bot detection based on news content as well as the spread diffusion process dynamics as a countermeasure for misinformation.
Keywords
Graph Classification, Graph Neural Networks, Machine Learning, Misinformation, Natural Language Processing, News propagation, Social Networks Analysis, Twitter Bots
Department
Computer Science
Recommended Citation
Thi Bui, Anson Pham, Warada Kulkarni, Yutong Yao, and Katerina Potika. "Classifying Real and Bot Users Based on their News Spread for Combating Misinformation" Proceedings - 17th IEEE International Conference on Service-Oriented System Engineering, SOSE 2023 (2023): 272-279. https://doi.org/10.1109/SOSE58276.2023.00038
