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This study provides a methodological framework for the computer to classifytweets according to variables of the Theory of Planned Behavior. We present asequential process of automated text analysis which combined supervisedapproach and unsupervised approach in order to make the computer to detect oneof TPB variables in each tweet. We conducted Latent Dirichlet Allocation (LDA),Nearest Neighbor, and then assessed "typicality" of newly labeled tweets inorder to predict classification boundary. Furthermore, this study reportsfindings from a content analysis of suicide-related tweets which identifytraits of information environment in Twitter. Consistent with extant literatureabout suicide coverage, the findings demonstrate that tweets often containinformation which prompt perceived behavior control of committing suicide,while rarely provided deterring information on suicide. We conclude byhighlighting implications for methodological advances and empirical theorystudies.

Theory-Driven Automated Content Analysis of Suicidal Tweets : Using Typicality-Based Classification for LDA Dataset