Multilevel User Credibility Assessment in Social Networks

Abstract: Online social networks are major platforms for disseminating both real and fake news. Many users, intentionally or unintentionally, spread harmful content, fake news, and rumors in fields such as politics and business. Consequently, numerous studies have been conducted in recent years to assess user credibility. A significant shortcoming of most existing methods is that they categorize users as either real or fake. However, in real-world applications, it is often more desirable to consider several levels of user credibility. Another limitation is that existing approaches only utilize a portion of important features, which reduces their performance. In this paper, due to the lack of an appropriate dataset for multilevel user credibility assessment, we first design a method to collect data suitable for assessing credibility at multiple levels. Then, we develop the MultiCred model, which places users at one of several levels of credibility based on a rich and diverse set of features extracted from users' profiles, tweets, and comments. MultiCred leverages deep LLMs to analyze textual data and deep neural models to process non-textual features. Our extensive experiments reveal that MultiCred significantly outperforms existing approaches in terms of several accuracy measures.