11 Pages Posted: 12 Oct 2013
Date Written: October 8, 2013
One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary users may never manage to reach a consensus on the description of the entities (e.g., books, user or songs) in the system. Yet, previous research has provided interesting evidence that the tag distributions of entities can become 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 stability of social tagging systems in a robust and methodical way? (ii) Does 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 making the following contributions: (i) we present a novel and robust method which overcomes a number of limitations in existing methods, (ii) we empirically investigate semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) we investigate potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language.
JEL Classification: E23,C92, D70, D83, M37
Suggested Citation: Suggested Citation
Wagner, Claudia Maria and Singer, Philipp and Strohmaier, Markus and Huberman, Bernardo A., Semantic Stability in Social Tagging Streams (October 8, 2013). Available at SSRN: https://ssrn.com/abstract=2337823 or http://dx.doi.org/10.2139/ssrn.2337823