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By Ciro Cattuto, Vittorio Loreto, and Luciano Pietronero Semiotic dynamics and collaborative tagging Present by Diyue Bu

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About Author: Ciro Cattuto My work focuses on measuring and understanding complex phenomena in systems that entangle human behaviors and digital platforms. I am interested in Computational Social Science, Data Science, Web Science, Infectious Disease Dynamics and Digital Epidemiology. I currently lead the Data Science Laboratory of the ISI Foundation, where I also serve as Research Director. I am a founder and a principal investigator of the SocioPatterns collaboration.ISI FoundationResearch DirectorSocioPatterns http://www.cirocattuto.info/

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Collaborative tagging The basic unit of information in a collaborative tagging system is a (user, resource, {tags}) triple tag-cloud https://delicious.com/ Cattuto, Ciro, Vittorio Loreto, and Luciano Pietronero. "Semiotic dynamics and collaborative tagging." Proceedings of the National Academy of Sciences 104.5 (2007): 1461-1464.

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Task and Result Task: extracting the resources associated with a given tag X and study the statistical distribution of tags cooccurring with X. Cattuto, Ciro, Vittorio Loreto, and Luciano Pietronero. "Semiotic dynamics and collaborative tagging." Proceedings of the National Academy of Sciences 104.5 (2007): 1461-1464.

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Stochastic Model: Zipfs law Result for this case similar to Zipfs law. Zipfs law: given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, etc.corpusnatural languageinversely proportional http://en.wikipedia.org/wiki/Zipf's_law

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Difference from Zipfs Law Difference: the low-rank part of the frequency-rank curves exhibits a flattening typically not observed in systems strictly obeying Zipfs law Reason: more general tags (semantically speaking) will tend to cooccur with a larger number of other tags. Cattuto, Ciro, Vittorio Loreto, and Luciano Pietronero. "Semiotic dynamics and collaborative tagging." Proceedings of the National Academy of Sciences 104.5 (2007): 1461-1464 http://en.wikipedia.org/wiki/Zipf's_law.

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A Yule-Simon Model with Long-Term Memory Modification: add power-law memory kernel to Yule-Simon model Supported by the test on correlation of time and tags occurrence Transformation: rich-get-richer Cattuto, Ciro, Vittorio Loreto, and Luciano Pietronero. "Semiotic dynamics and collaborative tagging." Proceedings of the National Academy of Sciences 104.5 (2007): 1461-1464 Where Q t (x) = a(t)/(x + ). a(t) is a normalization factor, and is a characteristic time scale over which recently added words have comparable probabilities. : semantic breadth

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Cattuto, Ciro, Vittorio Loreto, and Luciano Pietronero. "Semiotic dynamics and collaborative tagging." Proceedings of the National Academy of Sciences 104.5 (2007): 1461-1464. Comparison of theory & experimental result

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Result Interpretation users of collaborative tagging systems share universal behaviors that, despite the intricacies of personal categorization, tagging procedures, and user interactions, appear to follow simple activity patterns. Users behavior reveals two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each others tagging activity; (ii) a notion of memory, or aging of resources, in the form of a heavy-tailed access to the past state of the system

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Questions & Discussion What features of users behavior or collaborative tagging make the model related to memory kernel? What aspects of current online business pattern could be improved according to this finding on users behavior against collaborative tagging? What kind of positive & negative influences will this users behavior have? How to reduce negative influences by modifying the feedbacks users get from resource and tags? What would happen if the tag-cloud does not present each tags frequency of appearance (no difference on each words size)?