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“Big Data Trap”:Empirical Study of Big Data Bias on Social Media and Economic Consequence
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Title“Big Data Trap”:Empirical Study of Big Data Bias on Social Media and Economic Consequence  
AuthorWang Jianxin, Wu Shinong and Peng Diefeng  
OrganizationSchool of Business, Central South University;School of Management, Xiamen University;; 
Key WordsSocial Media; Sample Bias of Big Data; Investor Sentiment; Market Manipulation; Return Prediction 
AbstractTogether with the publishing of the special issue of big data on both Nature and Science, big data has been paid tremendous attention almost in all kinds of domain. As the rapidly evolving of the technology of Web2.0, social media data has also been applied to the investment strategies. Whether there is bias in user generated data on social network is still an open question. We try to test this question by examining the predictability of investor sentiment to stock market return. On measuring the investor sentiment in use of large-scale(649,636) discussion samples from the “Snowball” internet stock forum, we find: first of all, the aggregate investor sentiment fails to predict the stock market return. Secondly, market manipulators try to manipulate the data generating process on social media: the Granger Causal Test shows that manipulator’s sentiment is the Granger reason of the non-manipulator’s sentiment. Finally, manipulator’s sentiment negatively predicts the market return in short term, which indicates that the manipulators do gain abnormal return from the manipulating behavior. The empirical results indicate that the “pump and dumpers” are trying to manipulate the market price thorough posting purposely misleading information, which will bias the aggregate data systematically. Big data researches should be aware of the type of bias formed in the data-generating process.  
Serial NumberWP1211 
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