Identifying Real-world Credible Experts in the Financial Domain

Abstract

Establishing a solid mechanism for finding credible and trustworthy people in online social networks is an important first step to avoid useless, misleading, or even malicious information. There is a body of existing work studying trustworthiness of social media users and finding credible sources in specific target domains. However, most of the related work lacks the connection between the credibility in the real-world and credibility on the Internet, which makes the formation of social media credibility and trustworthiness incomplete. In this article, working in the financial domain, we identify attributes that can distinguish credible users on the Internet who are indeed trustworthy experts in the real-world. To ensure objectivity, we gather the list of credible financial experts from real-world financial authorities. We analyze the distribution of attributes of about 10K stock-related Twitter users and their 600K tweets over six months in 2015/2016, and over 2.6M typical Twitter users and their 4.8M tweets on November 2nd, 2015, comprising 1% of the entire Twitter in that time period. By using the random forest classifier, we find which attributes are related to real-world expertise. Our work sheds light on the properties of trustworthy users and paves the way for their automatic identification.