%0 Conference Paper %1 conf/cse/FuKK09 %A Fu, Wai-Tat %A Kannampallil, Thomas George %A Kang, Ruogu %B CSE (4) %D 2009 %I IEEE Computer Society %K cognitive_models conceptual_research pvv tagging topic_theory word_concept_relation %P 66-73 %T A Semantic Imitation Model of Social Tag Choices. %U http://www.humanfactors.illinois.edu/Reports&PapersPDFs/IEEESocialcom09/A%20Semantic%20Imitation%20Model%20of%20Social%20Tag%20Choices%20(2).pdf %V 4 %X 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

@inproceedings{conf/cse/FuKK09, 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}, added-at = {2010-01-21T09:49:46.000+0100}, author = {Fu, Wai-Tat and Kannampallil, Thomas George and Kang, Ruogu}, biburl = {https://www.bibsonomy.org/bibtex/29584d67f8a70ec991e9fd94d74cf2c80/pseiti}, booktitle = {CSE (4)}, crossref = {conf/cse/2009}, date = {2009-11-25}, description = {dblp}, ee = {http://dx.doi.org/10.1109/CSE.2009.382}, interhash = {5a94c6fb5e8317b0e6b29bfa96027d6c}, intrahash = {9584d67f8a70ec991e9fd94d74cf2c80}, keywords = {cognitive_models conceptual_research pvv tagging topic_theory word_concept_relation}, pages = {66-73}, publisher = {IEEE Computer Society}, timestamp = {2010-02-28T21:03:26.000+0100}, title = {A Semantic Imitation Model of Social Tag Choices.}, url = {http://www.humanfactors.illinois.edu/Reports&PapersPDFs/IEEESocialcom09/A%20Semantic%20Imitation%20Model%20of%20Social%20Tag%20Choices%20(2).pdf}, volume = {4 }, year = 2009 }