Bilkent University
Department of Computer Engineering
M.S. THESIS PRESENTATION
Misinformation Detection by Leveraging User Communities on Social Media
Oğuzhan Özçelik
Master Student
(Supervisor: Prof.Dr.Fazlı Can)
Computer Engineering Department
Bilkent University
Abstract: Social media platforms have become a primary source of accessing information. However, the spread of misinformation is inevitable due to the ease of creating and sharing malicious content, including fake news. Social media users in such platforms (e.g., Twitter) often find themselves exposed to similar viewpoints and tend to avoid contrasting opinions, particularly when connected within a community. To investigate this problem, we examine the presence of user communities and leverage them as a tool to detect misinformation on social media. In this thesis, we first collect tweets together with user engagements relevant to the recent events between 2020 and 2022. We then construct a human-annotated social media dataset having 5,284 English and 5,064 Turkish tweets with their veracity labels. After the data construction process, we leverage the presence of user communities for misinformation detection on social media. For this purpose, we propose a text similarity-based method that utilizes user-follower interactions within a social network to identify misinformation content. Our method first extracts important textual features of social media posts using contrastive learning. We then measure the similarity for each social media post, based on its 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 social media datasets and compare our method with the state-of-the-art approaches. The experimental results show that contrastive learning and user communities can effectively 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.
DATE: May 14, Tuesday @ 16:00 Place: EA 409