Self-disclosure topic model for classifying and analyzing Twitter conversations

Self-disclosure, the act of revealing one-self to others, is an important social be-havior that strengthens interpersonal rela-tionships and increases social support. Al-though there are many social science stud-ies of self-disclosure, they are based on manual coding of small datasets and ques-tionnaires. We conduct a computational analysis of self-disclosure with a large dataset of naturally-occurring conversa-tions, a semi-supervised machine learning algorithm, and a computational analysis of the effects of self-disclosure on subse-quent conversations. We use a longitu-dinal dataset of 17 million tweets, all of which occurred in conversations that con-sist of five or more tweets directly reply-ing to the previous tweet, and from dyads with twenty of more conversations each. We develop self-disclosure topic model (SDTM), a variant of latent Dirichlet al-location (LDA) for automatically classi-fying the level of self-disclosure for each tweet. We take the results of SDTM and analyze the effects of self-disclosure on subsequent conversations. Our model sig-nificantly outperforms several comparable methods on classifying the level of self-disclosure, and the analysis of the longitu-dinal data using SDTM uncovers signifi-cant and positive correlation between self-disclosure and conversation frequency and length. 1