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Social media are providing a huge amount of information, in scales never possible before. Sentiment analysis is a powerful tool that uses social media information to predict various target domains (e.g., the stock market). However, social media information may or may not come from trustworthy users. In order to utilize this information, a very first critical problem to solve is to filter credible and trustworthy information from contaminated data, advertisements or scams. We investigate different aspects of a social media user to score his/her trustworthiness and credibility. Furthermore, we provide suggestions on how to improve trustworthiness on social media by analyzing the contribution of each trust score. We apply trust scores to filter the tweets related to the stock market as an example target domain. While social media sentiment analysis has been on the rise over the past decade, our trust filters enhance conventional sentiment analysis methods and provide more accurate prediction of the target domain, here the stock market. We argue that while it is a failing to ignore the information social media provide, effectively trusting nobody, it is an equal failing to trust everybody on social media too: Our filters seek to identify whom to trust.
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