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Báo cáo khoa học: "Self-Disclosure and Relationship Strength in Twitter Conversations"

In social psychology, it is generally accepted that one discloses more of his/her personal information to someone in a strong relationship. We present a computational framework for automatically analyzing such self-disclosure behavior in Twitter conversations. Our framework uses text mining techniques to discover topics, emotions, sentiments, lexical patterns, as well as personally identifiable information (PII) and personally embarrassing information (PEI). | Self-Disclosure and Relationship Strength in Twitter Conversations JinYeong Bak Suin Kim Alice Oh Department of Computer Science Korea Advanced Institute of Science and Technology Daejeon South Korea jy.bak suin.kim @kaist.ac.kr alice.oh@kaist.edu Abstract In social psychology it is generally accepted that one discloses more of his her personal information to someone in a strong relationship. We present a computational framework for automatically analyzing such self-disclosure behavior in Twitter conversations. Our framework uses text mining techniques to discover topics emotions sentiments lexical patterns as well as personally identifiable information PII and personally embarrassing information PEI . Our preliminary results illustrate that in relationships with high relationship strength Twitter users show significantly more frequent behaviors of self-disclosure. 1 Introduction We often self-disclose that is share our emotions personal information and secrets with our friends family coworkers and even strangers. Social psychologists say that the degree of self-disclosure in a relationship depends on the strength of the relationship and strategic self-disclosure can strengthen the relationship Duck 2007 . In this paper we study whether relationship strength has the same effect on self-disclosure of Twitter users. To do this we first present a method for computational analysis of self-disclosure in online conversations and show promising results. To accommodate the largely unannotated nature of online conversation data we take a topic-model based approach Blei et al. 2003 for discovering latent patterns that reveal self-disclosure. A similar approach was able to discover sentiments Jo and Oh 2011 and emotions Kim et al. 2012 from user contents. Prior 60 work on self-disclosure for online social networks has been from communications research Jiang et al. 2011 Humphreys et al. 2010 which relies on human judgements for analyzing self-disclosure. The limitation of such