Browsing by Subject "Misinformation detection"

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Open Access
Misinformation detection by leveraging user communities on social media
(2024-05) Özçelik, Oğuzhan
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 on 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 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.
Open Access
SiMiD: similarity-based misinformation detection via communities on social media posts
(IEEE, 2024-01-02) Özçelik, Oğuzhan; Toraman, C.; Can, Fazlı
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.