Countering the Spread
An Approach to Identify Misinformation Spreaders in Social Media
Bibliographic Data
| ID | 22106984 |
|---|---|
| Authors | Antonela Tommasel (0000-0001-6091-8305, Centro Científico Tecnológico - Tandil), Juan Manuel Rodríguez (0000-0002-1130-8065, Aalborg University) |
| Year | 2025 |
| Volume | 12 |
| Issue | 5 |
| Pages | 3403-3415 |
| Publication date | 2025-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2025.3550029 |
| OpenAlex | W4408970897 |
| Language | EN |
| References cited | 45 |
Although social media generally provides a safe and enjoyable experience, it can also serve as a quick and easy means for spreading false news, misinformation, and other harmful content. These contents have been proven effective in influencing people’s beliefs and behaviors, spanning from influencing political opinions to directly impacting public health, particularly during events such as the COVID-19 pandemic. Then, it becomes crucial to take proactive measures to identify the misinformation spreaders to mitigate their impact and influence over society. Existing approaches primarily focus on identifying spreaders by analyzing features such as writing style, content, user profiles, and engagement statistics. However, since fake or deceiving content is frequently crafted to closely resemble authentic information, traditional techniques alone prove insufficient to effectively identify either fake content or its spreaders. In this context, this work introduces a deep-learning model tailored for detecting misinformation spreaders in social media. Our model not only incorporates content-based features but also integrates patterns of social interactions and information propagation structures. By considering the multifaceted nature of misinformation spread, our approach provides a more holistic and accurate means of identifying its spreaders. An experimental evaluation focusing on COVID-related data yielded promising results, demonstrating a significant performance improvement compared to other techniques in the literature. Thus, this research contributes to the ongoing efforts to develop robust tools for reducing the adverse effects of misinformation in social media
Advertising · Business · Computer security · Internet privacy · Microblogging · Misinformation · Social media · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Misinformation and Its Impacts · Spam and Phishing Detection
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Node2vec
Autoencoder-Based Feature Extraction for Identifying Hate Speech Spreaders in Social Media
FibVID
Believing and sharing misinformation, fact-checks, and accurate information on social media
Disinformation as a Threat to Deliberative Democracy
The Covid-19 Infodemic
| Citation velocity | historical |
|---|---|
| Highly cited | No |