Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo
Yue Hu, Jichang Zhao, Junjie Wu

TL;DR
This study uncovers a hidden group of ambivalent users on Weibo who frequently post mixed-emotion tweets, revealing their unique social behaviors and potential as targets for online marketing, based on emoticon analysis.
Contribution
It introduces the concept of ambivalent expression as an indicator of unusual social behaviors and demonstrates its implications for understanding online user dynamics.
Findings
Ambivalent users are mostly female and active at night or weekends.
They engage more in private communication and respond differently to public issues.
Their sentiment shifts from negative to positive, indicating mood regulation.
Abstract
Recent decades have witnessed online social media being a big-data window for quantificationally testifying conventional social theories and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden "ambivalent users" are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights or at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and thus serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports…
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