A Semantic Imitation Model of Social Tag Choices

—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 demon...

• Type: Conference
• Year: 2009
• Where: CSE
• Authors: Wai-Tat Fu, Thomas George Kannampallil, Ruogu Kang
• Keywords: Computer Science And Engineering, CSE 2009, Emergent Behavioral Patterns, Model, Social Tagging