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

Abstract

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.

The influence of firm’s feedbacks on user-generated content’s linguistic style matching–An explanation based on communication accommodation theory

“…Followed by the method proposed by Xiang et al (2022), this paper draws on the ideas of Ireland and Pennebaker (2010), combining Jieba lexical analysis with the Dictionary of Modern Chinese Functional Words to classify functional words into seven categories: auxiliary words, adverbs, prepositions, conjunctions, pronouns, orientation words, and tone words.…”

“…This paper is a valuable supplement to the existing research regarding the influences of firms' feedbacks frequency and text length on users' posting behaviors. Secondly, previous research is dedicated to conventional variables such as e-WOM and intention to purchase, but not enough attention has been paid to matching linguistic styles in VBCs (Xiang et al, 2022). This paper also opens up new ideas for future research on VBCs, as LSM in VBCs is vital for cultivating a shared sense of community (Ludwig et al, 2014;Zhang et al, 2021) and even for further community prosperity.…”

“…Customers' knowledge, information, and content in VBCs [also called user-generated content (UGC)] become a new source of channel information for firms to collect customer suggestions and make innovations (Chen et al, 2019) as the data generated by average users reveals their fundamental needs (Saura et al, 2021). Among the UGCs' traits, the language styles convey the customers' personalities, thoughts, or attitudes (Xiang et al, 2022), so that firms who understand or influence these styles can gain better market insights and improve the coherence between users and firms (Zhong and Schweidel, 2020). For example, Xiaomi, a leading mobile phone manufacturer in China, has created a unique "Xiaomi style" (Cao et al, 2020), attracting many devotees to constantly contribute their knowledge to the virtual community or even improve the sales performance by deciphering the information cues (Ludwig et al, 2013).…”

“…Participants interact linguistically in VBCs to facilitate smooth communication between participants and foster users' identification and loyalty to the brand (Santos et al, 2022). Based on topic modeling and linguistic styles, Xiang et al (2022) explored the reasons for producing various topics when posting in VBCs and proposed the concept of users' variety-producing behaviors. Wang et al (2022) explored the corpus of the product-centered language style of female consumers in China.…”

Exploring Trends in Environmental, Social, and Governance Themes and Their Sentimental Value Over Time

“…Since their inception by Blei et al (2003) , topic models have become one of the most important fields of modern machine learning in natural language processing (NLP). Topic modeling is gaining traction because it allows for the definition of potential topics drawing on large unstructured text data ( Indulska et al, 2012 ; Jeyaraj and Zadeh, 2020 ; Xiang et al, 2022 ). Storopoli (2019) explained the advantages of topic modeling as follows: First, topic modeling can be used to identify important topics that humans cannot discern.…”