Self-disclosure topic model for classifying and analyzing Twitter conversations

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Self-disclosure, the act of revealing oneself to others, is an important social behavior that strengthens interpersonal relationships and increases social support. Although there are many social science studies of self-disclosure, they are based on manual coding of small datasets and questionnaires. We conduct a computational analysis of self-disclosure with a large dataset of naturally-occurring conversations, a semi-supervised machine learning algorithm, and a computational analysis of the effects of self-disclosure on subsequent conversations. We use a longitudinal dataset of 17 million tweets, all of which occurred in conversations that consist of five or more tweets directly replying 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 allocation (LDA) for automatically classifying 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 significantly outperforms several comparable methods on classifying the level of selfdisclosure, and the analysis of the longitudinal data using SDTM uncovers significant and positive correlation between selfdisclosure and conversation frequency and length.
Publisher
Association for Computational Linguistics, SIGDAT
Issue Date
2014-10
Language
English
Citation

2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, pp.1986 - 1996

DOI
10.3115/v1/d14-1213
URI
http://hdl.handle.net/10203/314005
Appears in Collection
CS-Conference Papers(학술회의논문)
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