Issue |
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
|
|
---|---|---|
Article Number | 02010 | |
Number of page(s) | 6 | |
Section | Machine Learning, Deep Learning, and Applications | |
DOI | https://doi.org/10.1051/itmconf/20257302010 | |
Published online | 17 February 2025 |
Rumor Propagation and Detection Techniques in Social Networks
School of Artificial Intelligence, Wuchang University of Technology, Hubei, 430000, China
* Corresponding author: 20150775101@mail.sdufe.edu.cn
Social networks have become the core platform for information exchange in the new era, and their uniqueness is reflected in the extensive interconnections brought about by complex ties and technological progress. These characteristics jointly shape social networks and have a profound impact on the development of the new era. With the popularity of the Internet, the problem of online rumors has become increasingly prominent, bringing many negative effects on society. Due to the fast speed of information dissemination, extensive user participation, difficulty in verification, and significant group effects, online rumors can quickly spread in social networks and have an impact. Therefore, strengthening the supervision of network information is crucial to maintaining justice and order in cyberspace. The four leading rumor detection techniques are currently covered in this article, along with an introduction to the fundamentals of rumor detection. Prospects seem to offer fresh approaches to rumor detection in light of the potential difficulties facing societal development. Analyze the experimental data of different detection methods and explore the advantages and disadvantages of each technique. It is hoped that through in-depth research, information dissemination can be guaranteed, contributing to creating a safe and harmonious cyberspace.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.