SiMiD: Similarity-based Misinformation Detection via Communities on Social Media Posts

Date

2023-01-01

Author

Ozcelik, Oguzhan
Toraman, Çağrı
Can, Fazli

Metadata

Show full item record

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

Item Usage Stats

14
views

0
downloads

Cite This

Social media users often find themselves exposed to similar viewpoints and tend to avoid contrasting opinions, particularly when connected within a community. In this study, we leverage the presence of communities in misinformation detection on social media. For this purpose, we propose a similarity-based method that utilizes user-follower interactions within a social network to identify and combat misinformation spread. The method first extracts important textual features of social media posts via contrastive learning and then measures the cosine similarity per social media post based on their relevance to each user in the community. Next, we train a classifier to assess the truthfulness of social media posts using these similarity scores. We evaluate our approach on three real-world datasets and compare our method with six baselines. The experimental results and statistical tests show that contrastive learning and leveraging communities can effectively enhance the detection of misinformation on social media.

Subject Keywords

community detection, misinformation detection, social media analysis, social networks, transformers

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85183463826&origin=inward
https://hdl.handle.net/11511/109629

Collections

Department of Computer Engineering, Conference / Seminar