Özçelik, OğuzhanToraman, C.Can, Fazlı2024-03-072024-03-072024-01-02979-8-3503-2995-72831-7351https://hdl.handle.net/11693/114375Conference Name: 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS)Date of Conference: 21-24 November 2023Social 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.en-USMisinformation detectionSocial networksSocial media analysisCommunity detectionTransformersConference Paper10.1109/SNAMS60348.2023.10375480979-8-3503-1890-62831-7343