Semi-supervised Modeling of Social Actions in Online Dialogue

The emergence of discussion and debate on social media necessitates the development of new models for processing dialogue. Vitally important to inferring the social implicatures of dialogue on social media is to understand the social goals and desires of the participants. Thus, to infer social implicatures methods for capturing the social goals and intentions of the participants must be first developed. In this paper, we propose a set of fifteen social acts to infer the social goals of dialogue participants. Social acts capture the complex social actions individuals signal through their utterances. We present a semi-supervised algorithm called the Social Act Conversation Model (SACM) for the fifteen social acts. The algorithm is based on the premise that linguistic expressions in social dialogue relate directly to the topic being discussed or to the social actions of the participants. We show that incorporating the social acts identified by the SACM with existing pattern-based identification can increase the performance in inferring social implicatures (adversarial behavior, pursuing power, and leadership) for online dialogue communicated in English, Arabic, and Chinese.