The Wisdom of the Audience: An Empirical Study of Social Semantics in Twitter Streams
Interpreting the meaning of a document represents a fundamental challenge for current semantic analysis methods. One interesting aspect mostly neglected by existing methods is that authors of a document usually assume certain background knowledge of their intended audience. Based on this knowledge, authors usually decide what to communicate and how to communicate it. Traditionally, this kind of knowledge has been elusive to semantic analysis methods. However, with the rise of social media such as Twitter, background knowledge of intended audiences (i.e., the community of potential readers) has become explicit to some extents, i.e., it can be modeled and estimated. In this paper, we (i) systematically compare different methods for estimating background knowledge of different audiences on Twitter and (ii) investigate to what extent the background knowledge of audiences is useful for interpreting the meaning of social media messages. We find that estimating the background knowledge of social media audiences may indeed be useful for interpreting the meaning of social media messages, but that its utility depends on manifested structural characteristics of message streams.
KeywordsBackground Knowledge Topic Model Latent Dirichlet Allocation Twitter Message Audience User
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- 2.Paul Grice, H.: Logic and conversation. In: Cole, P. (ed.) Speech Acts. Syntax and semantics, vol. 3, pp. 41–58. Academic Press, New York (1975)Google Scholar
- 3.Hong, L., Davison, B.D.: Empirical study of topic modeling in twitter. In: Proceedings of the IGKDD Workshop on Social Media Analytics (SOMA) (2010)Google Scholar
- 4.Hotho, A., Staab, S., Stumme, G.: Wordnet improves text document clustering. In: Proc. of the SIGIR 2003 Semantic Web Workshop, pp. 541–544 (2003)Google Scholar
- 6.Litt, E.: Knock, knock. Who’s there? The imagined audience. Journal of Broadcasting and Electronic Media 56 (2012)Google Scholar
- 7.Marwick, A., Boyd, D.: I tweet honestly, i tweet passionately: Twitter users, context collapse, and the imagined audience. New Media and Society (2010)Google Scholar
- 8.McCallum, A.K.: Mallet: A machine learning for language toolkit (2002), http://mallet.cs.umass.edu
- 10.Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 695–704. ACM, New York (2011)Google Scholar
- 11.Searle, J.: A taxonomy of illocutionary acts, pp. 334–369. University of Minnesota Press, Minneapolis (1975)Google Scholar
- 12.Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 841–842. ACM, New York (2010)Google Scholar
- 13.Wagner, C., Strohmaier, M.: The wisdom in tweetonomies: Acquiring latent conceptual structures from social awareness streams. In: Semantic Search Workshop at WWW 2010 (2010)Google Scholar
- 14.Wallach, H.M.: Structured Topic Models for Language. PhD thesis, University of Cambridge (2008)Google Scholar