Regression-Based Microblogging Influence Detection Framework for Stock Market

Microblogs and social networks have become a valuable resource for mining sentiments in various fields. The sentiments posted on the web have reportedly influenced the trading and investment decisions and activities taking place in the stock exchanges. In this study, we have investigated explored the effects of microblogs on Chinese Stock Market. We have particularly focused on whether measurements of collective mood states (sentiments and persuasions) derived from large-scale microblogging posts are correlated to the values of Chinese stock market over a period of time. We have proposed a Regression-Based Microblogging Influence Detection Scheme (RMIDS) as a framework to detect the influence of microblogging posts on stock market in this paper. Our results showed that sentiments in microblogging has significant influence on the market, while the persuasion posts trying to interfere the stock market, by taking the advantage of lack of regulations in microblogging, do not succeed

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