Why Are User-Generated Contents So Varied? An Explanation Based on Variety-Seeking Theory and Topic Modeling

In online communities, such as Twitter, Facebook, or Reddit, millions of pieces of contents are generated by users every day, and these user-generated contents (UGCs) show a great variety of topics discussed that make the online community vivid and attractive. However, the reasons why UGCs show great variety and how a firm can influence this variety was unknown, which had been an obstacle to understanding and managing UGCs' variety. This study fills these two gaps based on variety-seeking theory and topic modeling, which is a technique in machine learning. We extract, quantitatively, the topic of the UGCs using topic modeling and divide UGCs into two types: single topic and multiple topics. The user's tendency to choose the type of UGC is used to measure variety-seeking behavior. We found that users have an intrinsic preference for variety when producing UGCs; the more single topic UGCs were produced in the past, the higher the probability of producing multiple topics UGC and the lower the probability of producing single topic UGC would be in the next, and vice versa. Furthermore, we discussed the effect of language/linguistic style matching (LSM) between firm feedbacks and UGCs on users' variety-seeking tendencies in UGCs' production. This study makes three contributions: (1) broadening variety-seeking theory to new behavior, that is content production behavior, and the results demonstrated that people would show a variety-seeking behavior in producing UGCs. (2) a new feasible method to measure the variety of UGCs by using topic modeling to extract the topics of UGCs and then measure the variety-seeking behavior in producing UGCs by analyzing the choice between single topic and multiple topics. (3) guidance for the firm to alter LSM of feedbacks to influence the variety of UGCs.