The Power of "We":The Impact of Language Style on User Involvement on an Online Knowledge Sharing Platform
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AbstractOnline knowledge sharing (e.g., Quora) has become a trending way for people to seek knowledge from each other on internet. Compared with traditional knowledge sharing organizations (e.g., schools, institutions), online knowledge sharing provides an open platform which allows knowledge to be shared and diffused in a bigger scale with a lower cost. Millions of people have contributed a huge amount of knowledge to those platforms in merely a few years and many online knowledge sharing platforms have been valued billions of US dollars. Despite the dramatic growth of online knowledge platforms, they are all facing an urging challenge: how to increase the users’ involvement on online knowledge platforms?
Our research taps into this challenge by studying shared knowledge on Zhihu.com, one of the largest online knowledge sharing platforms in China. By collecting a panel of 132,000 users’ shared knowledge (approximately 2 million answers on 1 million questions) since the first day of their registration, we investigate (1) what kind of shared knowledge users are most involved with? Specifically, what kind of shared knowledge attracted more “likes” and comments? (2) how does the language styles differ between most “liked” and least “liked” shared knowledge? and (3) how can the online sharing knowledge platform increase user involvement by matching users based on their language styles?
Using a panel data model with individual user fixed effects, we found that compared with the least “liked” shared knowledge, the most “liked” shared knowledge used a significant higher percentage of the “we” word. Similarly, those shared knowledge with more “we” word attracted more comments from other users. More interestingly, we found that other users’ comments on those most “liked” shared knowledge used a great amount of the “I” word but very few the “we” word. The different language styles we found can help online sharing knowledge platform increase user involvement by matching users based on their language styles.
Acceptance Date27/02/2019
All Author(s) ListTingting Fan, Leilei Gao
Name of Conference41st Annual ISMS Marketing Science Conference
Start Date of Conference20/06/2019
End Date of Conference22/06/2019
Place of ConferenceRome, Italy
Country/Region of ConferenceItaly
Year2019
LanguagesEnglish-United States
KeywordsSharing knowledge, Text Analysis

Last updated on 2019-27-09 at 10:42