Research Perspectives on Social Tagging

Social tagging has emerged as one of the most popular social software tools available online. Originating from Del.icio.us, social tagging capabilities can now be found on a number of major music, news, video, and commercial websites, as well as on social network sites and enterprise systems. Although social tagging allows individuals to organize content utilizing user-generated vocabulary, the power of social tagging stems from the ability to view and share resources with other users of the system. Through the sharing of tags and resources, social tagging systems facilitate network connections and perhaps even the creation of communities. In this panel, an exciting group of young researchers will present their ongoing work on social tagging. This panel will present a variety of perspectives on social tagging ranging from qualitative ethnographic work to quantitative visualizations. Additionally, the panel will cover topics such as: the definition of a tag, the role that tags play in social network sites, as well as tags in corporate and organizational settings. The research and the varying methods presented in this panel will present viewers with an exciting array of perspectives on social tagging. Additionally, in order to further engage the audience, the panelists will also participate in a point-counterpoint discussion with the participants which will help illuminate both the advantages and disadvantages of social tagging, as well as further highlight the multiple perspectives and approaches available for continuing social tagging research

Performance Tags- Who's running the show?

We describe a pilot study which specifically examines the prevalence and characteristics of performance tags on several sites. Identifying post-coordination of tags as a useful step in the study of this phenomenon, as well as other approaches to leveraging tags based on text and/or sentiment analysis, we demonstrate an approach to automation of this process, postcoordinating (segmenting) terms by means of a probabilistic model based around Markov chains. The effectiveness of this approach to parsing is evaluated with respect to the wide range of constructions visible on various services. Several candidate approaches for the latter stages of automated classification are identified