1 / 25

Marlon Pierce, Geoffrey Fox, Joshua Rosen, Siddharth Maini, and Jong Youl Choi

Social Networking for Scientists (Research Communities) Using Tagging and Shared Bookmarks: a Web 2.0 Application. Marlon Pierce, Geoffrey Fox, Joshua Rosen, Siddharth Maini, and Jong Youl Choi Community Grids Lab, Indiana University. Collaborative Technologies and Systems, 2008. Abstract.

rafael
Download Presentation

Marlon Pierce, Geoffrey Fox, Joshua Rosen, Siddharth Maini, and Jong Youl Choi

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Social Networking for Scientists (Research Communities) Using Tagging and Shared Bookmarks: a Web 2.0 Application Marlon Pierce, Geoffrey Fox, Joshua Rosen, Siddharth Maini, and Jong Youl Choi Community Grids Lab, Indiana University Collaborative Technologies and Systems, 2008

  2. Abstract • Report their investigation and implementation • Creating communities of researchers • Motivation is to provide outreach tools that broaden the participation of these groups in funded research activities

  3. Introduction • Enabling researchers to find both useful online resources and also potential collaborators on future research projects • MSI-CIEC is an NSF funded project to engage researchers at minority-serving institutions in modern cyberinfrastructure • “Minority Serving Institution-Cyberinfrastructure Empowerment Consortium” • The MSI-CIEC social networking Web portal combines social bookmarking and tagging with online curricula vitae profiles • Enable MSI researchers to find others with similar research interests

  4. Tag Clouds Click-TaggableGrants.gov RSS Feed

  5. Click a tag and see all associated links.

  6. Capabilities • The portal is designed to support academic user communities • online user profiles • shared online bookmarks • tags

  7. Capabilities Public user profiles with the user’s tag cloud RSS feeds Click tagging Bookmark any URL and users describe bookmarks with one or more keyword tags Users can search award funding and project data

  8. User’s tag cloud

  9. Social networking information RSS feeds

  10. Tagging

  11. Intended to foster research collaborations Interesting or uninteresting

  12. NSF Tag Cloud • The Tag Cloud displays all meta tags which were created automatically from the NSF Awards • You can browse all the projects related to these meta tags • Eg. Clicking “2007” would display all the people who have the tag “2007” meaning that they worked in projects awarded in the year 2007. • Meta Tags: • Year name “2008” • Size of the Project “Small” “Medium” “Large” • NSf Directorate “GEO” “BIO” etc.

  13. USER WHO HAVE TAG “2007” USER WHO HAVE TAG “2007”

  14. Researcher’s tag cloud and list of funded projects

  15. Tagging and Folksonomies

  16. Exploring communities in Collaborative Tagging Systems A user may want to see other people who have tagged on the same object Find a group of people who might have the same interest and look at their bookmarks or resources

  17. Models of Collaborative Tagging System • Main elements of collaborative tagging systems consist of tags, recourses, and users • Tag can be keywords, terms, or neologisms • Graphical connections

  18. Models of Collaborative Tagging System • General purpose of tagging systems • Find specific resources tagged collaboratively by multiple users • Retrieve information about resources or users

  19. Models of Collaborative Tagging System • Two different models: • Vector space model • Frequencies of tag occurrences for searching • Information retrieval • Graph model • Graphical characteristics • Path and degree of connectivity between nodes • Social network analysis

  20. Models of Collaborative Tagging System • Vector space model • A resource (or a user) is represented as a vector of tags • For example: • A resource tagged by 2 occurrences of tag1, 1 occurrence of tag2 • <2, 1>

  21. Models of Collaborative Tagging System • Graph model • Searching is task to find specific properties in the graph

  22. Discovering Communities Most common examples of social activities in a network is expressing one’s interests Finding a group of people who are working on the same topics or interests which we call “discovering a community”

  23. Frequency Analysis and Clustering • Find information: • More frequently used tags • More referenced resources • More actively involved users

  24. Structural Analysis • The structural analysis considers the tagging activities as a graph • More intuitive and human-understandable • Help users to find other information • Connectivity, connection distances between users • Size of communities • Degree of strength of a connection

  25. Summary and future work This paper describes the design and implementation of the MSI-CIEC Networking Portal This work is motivated by the need to support social networks of researchers Using the portal as a laboratory for core computer science work on social network analysis The key problem with most social network applications is the lack of interoperabiity

More Related