Jude Yew

From Shared Databases to Communities of Practice: A Taxonomy of Collaboratories

Promoting affiliation between scientists is relatively easy, but creating larger organizational structures is much more difficult, due to traditions of scientific independence, difficulties of sharing implicit knowledge, and formal organizational barriers. The Science of Collaboratories (SOC) project conducted a broad five-year review to take stock of the diverse ecosystem of projects that fit our definition of a collaboratory and to distill lessons learned in the process. This article describes one of the main products of that review, a seven-category taxonomy of collaboratory types. The types are: Distributed Research Centers, Shared Instruments, Community Data Systems, Open Community Contribution Systems, Virtual Communities of Practice, Virtual Learning Communities, and Community Infrastructure Projects. Each of the types is defined and illustrated with one example, and key technical and organizational issues are identified.

Knowing funny: genre perception and categorization in social video sharing

Categorization of online videos is often treated as a tag suggestion task; tags can be generated by individuals or by machine classification. In this paper, we suggest categorization can be determined socially, based on peoples interactions around media content without recourse to metadata that are intrinsic to the media object itself. This work bridges the gap between the human perception of genre and automatic categorization of genre in classifying online videos. We present findings from two internet surveys and from follow-up interviews where we address how people determine genre classification for videos and how social framing of video content can alter the perception and categorization of that content. From these findings, we train a Naive Bayes classifier to predict genre categories. The trained classifier achieved 82% accuracy using only social action data, without the use of content or media-specific metadata. We conclude with implications on how we categorize and organize media online as well as what our findings mean for designing and building future tools and interaction experiences.

Apples to oranges?: comparing across studies of open collaboration/peer production

This panel seeks to begin a discussion of how we can meaningfully compare and contrast between the diverse instances of open collaboration and peer production employed on the Internet today. Current research on the topic have tended to be too platform - (e.g. Wikipedia) or domain - (e.g. Open source) specific. The panelists will be tasked with addressing this problem using their own expertise and research projects to bear on the issue. Ultimately, the panel will seek to lay the foundations for the development of theoretical frameworks and principles for the design and application of open collaboration and CBPP based systems.

Know your data: understanding implicit usage versus explicit action in video content classification

In this paper, we present a method for video category classification using only social metadata from websites like YouTube. In place of content analysis, we utilize communicative and social contexts surrounding videos as a means to determine a categorical genre, e.g. Comedy, Music. We hypothesize that video clips belonging to different genre categories would have distinct signatures and patterns that are reflected in their collected metadata. In particular, we define and describe social metadata as usage or action to aid in classification. We trained a Naive Bayes classifier to predict categories from a sample of 1,740 YouTube videos representing the top five genre categories. Using just a small number of the available metadata features, we compare the classifications produced by our Naive Bayes classifier with those provided by the uploader of that particular video. Compared to random predictions with the YouTube data (21% accurate), our classifier attained a mediocre 33% accuracy in predicting video genres. However, we found that the accuracy of our classifier significantly improves by nominal factoring of the explicit data features. By factoring the ratings of the videos in the dataset, the classifier was able to accurately predict the genres of 75% of the videos. We argue that the patterns of social activity found in the metadata are not just meaningful in their own right, but are indicative of the meaning of the shared video content. The results presented by this project represents a first step in investigating the potential meaning and significance of social metadata and its relation to the media experience.

Location aware applications to support mobile food vendors in the developing world

This paper describes an ongoing research project to explore the potential of location aware mobile phone-based applications to support mobile food vendors in the developing world. These vendors are a ubiquitous phenomenon in the developing world and can be seen hawking their wares in carts, bicycles or motorcycles. We report preliminary findings from nine interviews conducted with various mobile food vendors in Indonesia. Based on these findings, we present our initial system design for a mobile phone-based application that allows these vendors to advertize their current location, accept orders from customers, and have customers recommend particular vendors.

Twiki and wetpaint: two wikis in academic environments

This paper describes a community-based effort to preserve organizational knowledge and to orientate newcomers to a graduate school. It presents a very brief review of recent research on wiki use in corporate and organizational environments and initial data from two wiki implementation iterations within our academic community. We contrast use of a TWiki with that of a WetPaint wiki. Our data suggest that with low barriers to participation and a great deal of patience, wikis can be useful stores for community information and knowledge sharing.