CCR : A Model for Sharing Reputation Knowledge Across Virtual Communities
Working with private identities of separate virtual communities (Tric -Deustche Telecom-) and reputation mechanisms to share information among different communities. The process is formed by 3 blocks
1.- Enabling preconditions
- category matching level: [0,1] value representing community correlation based
- domain confidence
Interesting: one of the measures ids is based on Shannon entropy of the domains (I need o revise that again)
2.- Conversion of reputation values
3.- Attribute matching (some kind of ontology alignment) as a [0,1] value, with some confidence level (certainty)
Showing an example of travel agencies :-), looking for a hotel in Milano, using communities in Trip Advisor, Expedia and Booking.
Monetizing User Activity on Social Networks – Challenges and Experiences
Meenakshi Nagarajan, Kamal Baid, Amit Sheth, and Shaojun Wang
Well-known monetization models for the Web, but not easy on Web 2.0 and SNS. Actually, advertisement-based models are used with marginal success, due to (i) informal nature of content, non-policed content and because people are there to network. So, at least, the ads have to consider: (i) identify monetizable posts (intents behind users posts) and (ii) identifying keywords on user’s comments.
Consider that people write sentences, not keywords or phrases, so the system has to be ready to analyze that to locate action patterns around entities. The example: how people seek for information. Patterns used are «where do I find…. does anyone know how… someone tell me where…» So all these patters matches with a seeking pattern in the Candidate Pool and the system can identify the question (this is very similiar to dialog characterization for agents -see AIWS slides-)
Monetization potential is calculated from this seeking score and calculating a transactional intention score for each one of them. All test has been done off-line!! What about the cost? Because it’s important to do that on-line.
A Composite calculation for author activity in Wikis: accuracy needed
Interesting title, but I barely can hear her :-(… oh!, better with the microphone.
Analysis of social interaction spaces (wikis, blogs, twitter…) evaluation activity, dynamics, identifying communities and topics, so they can improve existing infrastructures. They use SONIVIS as visualization tools. And today she is going to speak about wikis. Motivation: to evaluate author activity. They activity is characterized by (i) the number of changes/versions, (ii) a betweenness centrality measure among authors, (iii) significant content: frequency an author has added a term to a page and the importance of this term. This measure is normalized. These values are combined in the final author contribution. This measure is dynamic, so a cumulative author contribution can be calculated.
The example uses the English Wikipedia articles about virtual reality. A six-month period has been analyzed. Results are in the paper.
Model for Voter Scoring and Best Answer Selection in Community Q&A Services
Chung Tong Lee
The problem: how to select the best answer in Q&A communities (as Yahoo!Answers). A Voting Score mechanism is presented, based on a fixed point basis.
Voting is affected by (i) social bias -vote based on answerer, not in the quality of the answer, (ii) personal gain. The formula for the voting and the fixed point characterization is presented (see the paper). Some examples to model the voting (simulations) using random voting and ballot stuffing simulation. Zipf’s law used for generate random votes (entropy measure). Results again in the paper.