The document discusses improving Wikipedia talk pages through semantic structuring and social analysis. It presents research on understanding how talk pages are used through interviews and analyzing 100 pages. A semantic model is developed to represent talk page content and structure. The model can be used to semantically mark-up talk pages and extract socially useful information through queries. The goal is to increase coordination and understanding of discussions through adding semantic structure.
What Are The Drone Anti-jamming Systems Technology?
Understanding and improving Wikipedia article discussion spaces SAC2011
1. Understanding and Improving Wikipedia Article Discussion Spaces Jodi Schneider , Alexandre Passant, John Breslin ACM SAC 2011-03-24 Taichung, Taiwan
2. Wikipedia editors are leaving faster than they can be replaced Felipe Ortega via http://www.businessinsider.com/chart-of-the-day-wikipedia-editors-2009-11 of 27
11. of 27 Classification Example Reference to... Sources outside the wiki ... Not sure where to put it but I’ll leave it here as somebody might find it useful Reverts, removed material, or controversial edits I noticed some people edit the page into what it will be in 10 minutes but someone is reverting it...just let it be Edits the discussant made Added the About.com review since the review was part of the reception section. Requests for... Help with another article, portal, etc. This is just to invite attention to the page Facebook statistics just created…
"only 10% of participants knew that Wikipedia has a policy against posting original research.” – Antin & Cheshire, CSCW 2010
Talk pages are LONG!!! six Talk pages can yield over 100 printed pages [3], and individual Talk pages may yield 50 printed pages.
Trust & credibility layer Golbeck, Computing with Social Trust, Springer 2008 Hartig, Querying Trust in RDF Data with tSPARQL, ESWC 2009 W3C Provenance Incubator Group Final Report
2 Wikipedia editors, 2 Wikipedia administrators
20 pages per category
Partial example of RDFa markup
We can also retrieve posts by novices, or which have no replies. Or both! SELECT ?comment ?reply ?user ?name WHERE { ?comment a sioc:Post ; sioc:has_creator ?user . OPTIONAL { ?user sioc:name ?name . } OPTIONAL { ?comment sioc:has_reply ?reply . } FILTER (!BOUND(?name)) FILTER (!BOUND(?reply)) }
