Detecting Misinformation on Social Media using Community Insights and Contrastive Learning

Date

2025-02-17

Author

Ozcelik, Oguzhan
Toraman, Çağrı
Can, Fazli

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Social media users are more likely to be exposed to similar views and tend to avoid contrasting views, especially when they are part of a community of social media users. In this study, we investigate the presence of user communities and leverage them as a tool to detect misinformation on social media, specifically on X (formerly known as Twitter). We propose a misinformation detection framework, namely Similarity-based Misinformation Detection (SiMiD) that employs microblogs and utilizes user-follower interactions within a social network. Our approach extracts important textual features of social media posts using a transformer-based language model. We use contrastive learning and pseudo-labeling to fine-tune the language model. Then, we measure the similarity for each social media post based on its relevance to each user in the communities. Finally, we train a machine learning model to identify the truthfulness of social media posts using these similarity scores. We evaluate our approach on three social media datasets, compare our method with twelve state-of-the-art approaches, and answer five research questions. The experimental results, supported by statistical tests, show that contrastive learning and user communities can enhance the detection of misinformation on social media. Our model can identify misinformation content by achieving a consistently high weighted F1 score of over 90% across all datasets, even employing only a small number of users in communities. We make our implementations publicly available and provide all details that are necessary for the reproducibility of experiments.1

Subject Keywords

filter bubble, misinformation detection, social media analysis, social networks, text similarity, user communities

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105003476149&origin=inward
https://hdl.handle.net/11511/114700

Collections

Department of Computer Engineering, Article