Facilitating exploratory search by model-based navigational cues
Proceedings of the 15th international conference on Intelligent user interfaces
Exploratory information search by domain experts and novices
Proceedings of the 15th international conference on Intelligent user interfaces
Cognitive models of user behavior in social information systems
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Semantic imitation in social tagging
ACM Transactions on Computer-Human Interaction (TOCHI)
Friendship, collaboration and semantics in Flickr: from social interaction to semantic similarity
Proceedings of the International Workshop on Modeling Social Media
Learning by foraging: The impact of individual knowledge and social tags on web navigation processes
Computers in Human Behavior
Implicit and explicit memory in social tagging: evidence from a process dissociation procedure
Proceedings of the 29th Annual European Conference on Cognitive Ergonomics
Implicit imitation in social tagging: familiarity and semantic reconstruction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Can simple social copying heuristics explain tag popularity in a collaborative tagging system?
Proceedings of the 5th Annual ACM Web Science Conference
Exploratory search with semantic transformations using collaborative knowledge bases
Proceedings of the 7th ACM international conference on Web search and data mining
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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.