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
%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 tagging topic
%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 = {2010-05-11T20:43:51.000+0200},
author = {Fu, Wai-Tat and Kannampallil, Thomas George and Kang, Ruogu},
biburl = {https://www.bibsonomy.org/bibtex/29584d67f8a70ec991e9fd94d74cf2c80/kasimiro},
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 tagging topic},
pages = {66-73},
publisher = {IEEE Computer Society},
timestamp = {2010-05-11T20:43:51.000+0200},
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
}