Using Historical Social Media Retrieved Trust Attributes to Help Distinguishing Trustworthy Users

With the penetration of social media across the world, the information generated by the users has increased exponentially. The wisdom of crowds can now be easily accessed from the Internet. The problem is, how to correctly interpret the true public opinion without distorting it? Considering the spam or malicious users hidden in the social media spreading misinformation and disinformation, the solution might not be trivial. Some previous work uses quantity accumulation trying to mitigate the influence of bad users. More recent works add machine learning techniques to help with the correct judgment. However, the importance of the individual user - the actual person who is behind the screen, does not attract the attention it deserves. In this work, focusing on the history of user behavior, we provide a different angle to understand the connection between the credibility of social media users and the trustworthiness of their virtual representatives. By analyzing Twitter data from November 2017 to November 2021 which contains three types of users (typical, topic-related, and expert) on two target domains (politics and finance), we can gain deeper insights on the social media users and their trustworthiness.

Access Publication: Download PDF of Report