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It Is an Equal Failing to Trust Everybody and to Trust Nobody: Stock Price Prediction Using Trust Filters and Enhanced User Sentiment on Twitter

Published:13 September 2019Publication History
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Abstract

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. 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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      • Published in

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 19, Issue 4
        Special Section on Trust and AI and Regular Papers
        November 2019
        201 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3362102
        • Editor:
        • Ling Liu
        Issue’s Table of Contents

        Copyright © 2019 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 13 September 2019
        • Accepted: 1 May 2019
        • Revised: 1 April 2019
        • Received: 1 November 2018
        Published in toit Volume 19, Issue 4

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