Semiotic dynamics and collaborative tagging

By Ciro Cattuto, Vittorio Loreto, Luciano Pietronero. Proceedings of the National Academy of Sciences of the United States of America, 2006

391 citations recorded by the source corpus.

Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag cooccurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's 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. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features with a surprisingly high accuracy. This points in the direction of a universal behavior of users who, despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity, appear to follow simple activity patterns.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2f6fae5202872e94d9ebfb9944ecf9e61ee5ce57

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Citations in the corpus

The folksonomy tag cloud: when is it useful? (2008, methodology): “As folksonomy frequency tends to follow a power law distribution [15], we made the size of each tag proportional to the log of its frequency.”
Competition among memes in a world with limited attention (2012, methodology): “In the context of single-agent behavior, our memory mechanism is reminiscent of the classic Yule-Simon model , \cite{yule-simon 43 , Cattuto30012007 44 }.”
Recommender Systems (2012, background): “The social impact [272] and dynamical properties of folksonomies [273,274] are expected to be applied to obtain trustworthy and real-time recommendations in tagging systems.”
Solving the apparent diversity-accuracy dilemma of recommender systems (2008, positive): “Topical information and other measures of item-item similarity can be used to further diversify recommendation lists [24]: usergenerated classifications such as tags [33, 34, 35] may be useful here.”