One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language streams alone.
While research on collaborative tagging systems has largely been the purview of computer scientists, the behavior of these systems is driven by the psychology of their users. Here we explore how simple models of boundedly rational human decision making may partly account for the high-level properties of a collaborative tagging environment, in particular with respect to the distribution of tags used across the folksonomy. We discuss several plausible heuristics people might employ to decide on tags to use for a given item, and then describe methods for testing evidence of such strategies in real collaborative tagging data. Using a large dataset of annotations collected from users of the social music website Last.fm with a novel crawling methodology (approximately one millions total users), we extract the parameters for our decision-making models from the data. We then describe a set of simple multi-agent simulations that test our heuristic models, and compare their results to the extracted parameters from the tagging dataset. Results indicate that simple social copying mechanisms can generate surprisingly good fits to the empirical data, with implications for the design and study of tagging systems. 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. Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge. We present a semantic imitation model of social tagging and exploratory search based on theories of cognitive science. The model assumes that social tags evoke a spontaneous tag-based topic inference process that primes the semantic interpretation of resource contents during exploratory search, and the semantic priming of existing tags in turn influences future tag choices. The model predicts that (1) users who can see tags created by others tend to create tags that are semantically similar to these existing tags, demonstrating the social influence of tag choices; and (2) users who have similar information goals tend to create tags that are semantically similar, but this effect is mediated by the semantic representation and interpretation of social tags. Results from the experiment comparing tagging behavior between a social group (where participants can see tags created by others) and a nominal group (where participants cannot see tags created by others) confirmed these predictions. The current results highlight the critical role of human semantic representations and interpretation processes in the analysis of large-scale social information systems. The model implies that analysis at both the individual and social levels are important for understanding the active, dynamic processes between human knowledge structures and external folksonomies. Implications on how social tagging systems can facilitate exploratory search, interactive information retrievals, knowledge exchange, and other higher-level cognitive and learning activities are discussed. 