Start Date
11-8-2016
Description
Envy is receiving more and more attention from both the IT industry and academia. IT researchers usually rely on subjective, survey-based methods to study envy. In the current paper, we investigated envy-related messages from Twitter through text analytics. We found that online envy was significantly associated with social media use patterns: users who “favorite” more and post less are more likely to express malicious envy while users who “favorite” less and post more are more likely to express benign envy. Moreover, users’ influence moderates the degree of the two types of envy. When a user is a profound influencer, the user would be more likely to express a lower level of malicious envy, and would also be more likely to express a higher level of benign envy. The findings provide a number of theoretical contributions and practical implications.
Recommended Citation
Wu, Jiao; Srite, Mark; and Deng, Shuyuan, "Tweet, Favorite, and Envy" (2016). AMCIS 2016 Proceedings. 16.
https://aisel.aisnet.org/amcis2016/SocialTech/Presentations/16
Tweet, Favorite, and Envy
Envy is receiving more and more attention from both the IT industry and academia. IT researchers usually rely on subjective, survey-based methods to study envy. In the current paper, we investigated envy-related messages from Twitter through text analytics. We found that online envy was significantly associated with social media use patterns: users who “favorite” more and post less are more likely to express malicious envy while users who “favorite” less and post more are more likely to express benign envy. Moreover, users’ influence moderates the degree of the two types of envy. When a user is a profound influencer, the user would be more likely to express a lower level of malicious envy, and would also be more likely to express a higher level of benign envy. The findings provide a number of theoretical contributions and practical implications.