Abstract
The popularization of social media exposes the structure of people’s conversation - what kind of people speak with whom, on what topics and with what kinds of words. In this paper, we propose a new approach to mining conversational network by community analysis, which exploits users’ profile information, interaction network and linguistic usage. Using our framework, we conducted empirical analysis on the complex relation among people’s profile information, social network, and language network using a large dataset from Twitter, which covers more than 7M people. Our findings include (1) we can extract a community composed of people who use the same kinds of slangs by exploiting information from both the social network and word usage, (2) when we focus on similarity among communities in terms of both interaction and word usage, we can find specific patterns based on the people’s profile information including their attributes and interests.
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Marui, J., Nori, N., Sakaki, T., Mori, J. (2014). Empirical Study of Conversational Community Using Linguistic Expression and Profile Information. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, YS. (eds) Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Springer, Cham. https://doi.org/10.1007/978-3-319-09912-5_24
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DOI: https://doi.org/10.1007/978-3-319-09912-5_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09911-8
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