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This research seeks to answer two ever-pressing questions for those who rely on social media for information – What information can I trust? and Who can I trust? Specifically, this research addresses how to recommend trustworthy information from specific information sources using an innovative method of combining interpersonal trust on the Internet and classification algorithms for various target domains. A variety of domains have been studied to see how information from social media can predict or explain the phenomena of the domain, from politics (who wins the next election) to finance (what happens to a company’s stock). It is important, however, that as we improve our methods to interpret the information retrieved from social media, we also pay attention to the quality of that information. Social media information is noisy and possibly contains useless, misleading, or even malicious information distributed by untrustworthy users. It is paramount to filter credible and trustworthy information generated by trustworthy users and domain experts from contaminated data, advertisements, or scams. In this paper, we develop a novel, domain-independent method to distinguish three types of social media (specifically Twitter) users from one another: typical users, domain-related users, and experts. We take a comprehensive list of trust attributes (i.e., measurable properties of a social media user account or posts such as the number of tweets or the number of followers) from previous work and study the value of these trust attributes for the three types of users. The value of the trust attribute for typical users is the average value for all the users. Domain-related users are those who tweet with a set of domain-related handles (e.g., @a_stock_ticker). Experts are real world experts of the filed, who we extract from reputable journalistic sources outside social media. By applying random forest to compare trust attribute performance, we identify which trust attributes can best distinguish expert, domain-related, and typical users from one another. Most importantly, we keep our work independent of the subject domain by performing the same set of experiments in the finance as well as the politics domain. We compare the distributions of trust attributes between finance and politics domains, to test if the classification method can be applied to various target domains and still provide robust results. Overall, we identify trust attributes capable of distinguishing trustworthy users from malicious ones, or to act as trust filters. Our work increases the reliability and utility of social media data for better decision-making. By applying trust filters to filter out untrustworthy and malicious users, the bad impact of social media such as fake news or media manipulation can thus be minimized.
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