Data Analytics for Business and Societal Challenges
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Paper Number
1785
Paper Type
Completed
Description
We examine the value of crowd-generated content on a financial social media platform. The value of such content is measured by the incremental accuracy of using the cues from those content to predict stock volatility. We argue that the characteristic features of a crowd, such as crowd size, crowd diversity, and crowd independence, have significant impacts on the predictive value of the subsequent content generated by the crowd. Leveraging a natural experiment setup where the financial platform no longer receives cross-postings from another major social media platform, we show empirical evidence that crowd size determines the predictive value of the corresponding crowd-generated content. Furthermore, the impact of the characteristic size on the value of generated content is likely heterogeneous, i.e., moderated by crowd diversity and independence. We provide discussions about the implications of this study to the value of crowd-generated data, crowd wisdom, and digital platform competition.
Recommended Citation
Zhang, Xiaohui; Du, Qianzhou; and Zhang, Zhongju, "What Types of Crowd Generate More Valuable Content? Evidence from Cross-Platform Posting" (2021). ICIS 2021 Proceedings. 9.
https://aisel.aisnet.org/icis2021/data_analytics/data_analytics/9
What Types of Crowd Generate More Valuable Content? Evidence from Cross-Platform Posting
We examine the value of crowd-generated content on a financial social media platform. The value of such content is measured by the incremental accuracy of using the cues from those content to predict stock volatility. We argue that the characteristic features of a crowd, such as crowd size, crowd diversity, and crowd independence, have significant impacts on the predictive value of the subsequent content generated by the crowd. Leveraging a natural experiment setup where the financial platform no longer receives cross-postings from another major social media platform, we show empirical evidence that crowd size determines the predictive value of the corresponding crowd-generated content. Furthermore, the impact of the characteristic size on the value of generated content is likely heterogeneous, i.e., moderated by crowd diversity and independence. We provide discussions about the implications of this study to the value of crowd-generated data, crowd wisdom, and digital platform competition.
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