Abstract
In recent years, SNS has become a service that everyone uses. In this study, we analyze Twitter, one of the most popular SNSs, which allows users to post their daily events and feelings within 140 characters and is used by people all over the world. In this study, we investigate the relationship between SNS posts and latent approval needs. The linguistic features of tweets and their contextual features are analyzed using information such as the frequency of posts and the number of characters in tweets, and the degree of desire for approval is defined and quantified based on the results of the analysis of tweets. The experiment results show that the agreement between the naïve Bayes classifier and human ratings was about 60%. It is found that users with a high percentage of posts for approval desire tend to post less frequently and with a higher average number of characters. This indicates that it may be because these users post for approval desire when it is important or when they really want to say something.
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Murata, E., Tago, K., Jin, Q. (2022). Linguistic and Contextual Analysis of SNS Posts for Approval Desire. In: Meiselwitz, G. (eds) Social Computing and Social Media: Design, User Experience and Impact. HCII 2022. Lecture Notes in Computer Science, vol 13315. Springer, Cham. https://doi.org/10.1007/978-3-031-05061-9_24
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DOI: https://doi.org/10.1007/978-3-031-05061-9_24
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