NDLI: A Semantic Imitation Model of Social Tag Choices

Content Provider	IEEE Xplore Digital Library
Author	Wai-Tat Fu Kannampallil, T.G. Ruogu Kang
Copyright Year	2009
Abstract	We describe a semantic imitation model of social tagging that integrates formal representations of semantics and a stochastic tag choice process to explain and predict emergent behavioral patterns. The model adopts a probabilistic topic model to separately represent external word-topic and internal word-concept relations. These representations are coupled with a tag-based topic inference process that predicts how existing tags may influence the semantic interpretation of a document. The inferred topics influence the choice of tags assigned to a document through a random utility model of tag choices. We show that the model is successful in explaining the stability in tag proportions across time and power-law frequency-rank distributions of tag co-occurrences for semantically general and narrow tags. The model also generates novel predictions on how emergent behavioral patterns may change when users with different domain expertise interact with a social tagging system. The model demonstrates the weaknesses of single-level analyses and highlights the importance of adopting a multi-level modeling approach to explain online social behavior.
Starting Page	66
Ending Page	73
File Size	5687229
Page Count	8
File Format	PDF
ISBN	9781424453344
DOI	10.1109/CSE.2009.382
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Stability Computational modeling Semantic Imitation Stochastic processes Human factors Predictive models Power system modeling Multi-level Social Behavior Modeling Tagging Frequency Computational Cognitive Model Cognitive science Social Tagging
Content Type	Text
Resource Type	Article

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