Elizeu Santos-Neto, Flavio Figueiredo Jussara Almeida, Miranda Mowbray Marcos Gonçalves, Matei Ripeanu The 2 nd IEEE SocialCom/SIN -- August 2010

Decentralized, Collaborative & Non-proprietary [1] 2 [1] Y. Benkler. “The Wealth of Networks”. Yale University Press (2006). Commons-based Peer Production Systems OfflineOnline Car pooling Information Sharing Resource Sharing Volunteer Firefighters BitTorrent OurGrid.org Wikis Tagging Q&A Portals

3 Tagging Systems Bandwidth Time ExpertiseCPU Annotation = Tags + Items Tags are free-form words Items can be virtually anything URLs, photos, videos, citation records, etc… Photos Annotations

Increasingly popular Millions of users [2] GBytes of content and annotations daily [2] User-generated metadata = new opportunities To improve existing systems (e.g., social search) To create new mechanisms (e.g., reputation systems) Open problem: how to quantify the value of user contributions in these systems? 4 [2] R. Ramakrishnan, A. Tomkins. “Toward a PeopleWeb”. IEEE Computer 40(8): (2007)

5 To define a method that quantifies the value of users’ contribution in tagging systems that is accurate, feasible and robust. What is the tolerance to malicious users? What is the computational complexity? How close is it to the true value of user contribution?

Problem formalization A solution framework A method to quantify the value of tags Entropy-based metric Algorithms to compute the metric 6

Introduction & Motivation Solution Framework Value of TagsEvaluation Conclusions & Future Work

Contribution = Tags + Items Value of tags Context: navigation/search Intuition: value ≈ improvement on navigation/search Value of an item Context: item usage Intuition: value ≈ usefulness to a user 8 Content Annotations

9 Info Seeker Relevant Item Set Finder Contribution Value Item Usage Monitor Tag Value Calculator Info Producer Item Value Calculator Tag Value Aggregator Item Value Aggregator Information Needs Past activity Tags Items Usage Values The Value of User Contribution Information flow

Introduction & Motivation Solution Framework Value of TagsEvaluation Conclusions & Future Work

Intuition: tags are valuable if they narrow the scope of navigation, while retrieving relevant items. 11 Items in the system Relevant items to an info seeker Items retrieved by a set of tags Value of tags is proportional to this intersection Tags published by an info producer

Introduction & Motivation Solution Framework Value of TagsEvaluation Conclusions & Future Work

Feasibility: is the method efficient? Accuracy: is the estimation close to the real value? Robustness: can users boost their contributions maliciously? 13

14 Only 4% of users have more than 100 unique tag assignments.

15 80% of users have NOT produced tags/items in 1 moth or more.

Introduction & Motivation Solution Framework Value of TagsEvaluation Conclusions & Future Work

Assessing the value of user contributions in social tagging systems is a relevant and challenging problem This work … …proposes a solution framework …provides preliminary results on feasibility Current efforts… Evaluating techniques that estimate relevant items Designing algorithms to calculate and aggregate value 17

Algorithms Exploit user activity similarity Aggregation method that exploits implicit social relations Evaluation Accuracy – does the estimated value match a ground truth? Robustness – what about spammers? Value of items Build mechanisms that harness the value of contributions Spam detection Social search 18

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