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Building a-standpoints-web-to-support-decision-making-in-wikipedia--cscw2012-doctoral-colloquium


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CSCW2012 talk to doctoral colloquium. Building a standpoints web to support decision-making in Wikipedia.

CSCW2012 talk to doctoral colloquium. Building a standpoints web to support decision-making in Wikipedia.

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  • Screenshot of the article Heath Totten
  • 72/day on average
  • Adding funding/collaborators slide.
  • Mixed-initiative Generate argument maps from conversations (Arvina, MAgtALO) Populate a knowledge base Maybe change your views
  • They don’t say how they extracted these – but they say Someone makes statement (1) Someone else gives (4) as a reason/premise for (1) Someone else gives (3) as an additional reason for (1) (2) Is a counterproposal with a range of supporting reasons === Icons:
  • Maximal consistent sets
  • detect the prevalence of knowledge, emotion, and values as a first approximation to the purpose. High sentiment and low sentiment messages can be found through sen- timent analysis [21], which we also use as a first indication of whether people agree and how strongly their views are expressed. Values are abstract qualities such as utility, beauty, respect, and patriotism; these can be found with gazetteers. Knowledge-based discussions often cite statistics, experts, and studies, which can be text-mined; they may also commonly use argumentation schemes such as expert opinion.
  • Detecting the purpose of the discussion… Using keywords and rhetorical analysis Provides context
  • Isn’t it funny that people tweet about this
  • Task-specific conversation
  • Transcript

    • 1. Digital Enterprise Research Institute Building a Standpoints Web to Support Decision-Making in Wikipedia Jodi Schneider Doctoral Colloquium at CSCW 2012 2012-02-12 Seattle, Washington© Copyright 2010 Digital Enterprise Research Institute. All rights reserved.
    • 2. What I’m looking forDigital Enterprise Research Institute  Scoping & focus  Detailed mentoring on CSCW/HCC methodologies  Interviewing  Qualitative Research  Statistics  Suggestions for evaluating my work 2
    • 3. Digital Enterprise Research Institute 3
    • 4. Should we delete this article?Digital Enterprise Research Institute 4
    • 5. Improving deletion discussionsDigital Enterprise Research Institute  Main problems:  Newcomers who don’t know how to argue  Overwhelm of long discussions  Discussions that happen over and over again  Deletion as quality control  Large number of discussions - ~500/week 5
    • 6. Deletion argumentDigital Enterprise Research Institute [Delete the article]...hasnt played since 2008. His 66-73 record is far from stellar and, in my opinion, does not merit an article. >>He pitched last month and plays for the Venezuelan League. This meets our article criteria. 6
    • 7. GoalsDigital Enterprise Research Institute  Newcomers who don’t know how to argue  Characterize the “good” and “bad” arguments  Develop argument templates  Provide guidance and support for new users in properly structuring arguments according to Wikipedia’s rhetorical standards  Overwhelm of long discussions  Develop a claims/argument explorer  Discussions that happen over and over again  Prototype an argument bot  Populate argument maps with mixed-initiative claims extraction 7
    • 8. OverviewDigital Enterprise Research Institute  Corpus:  All Wikipedia deletion discussions from January 29, 2011  Perspectives/approaches:  Argumentation  CSCW/HCC  Text analytics  Ontologies/Social Semantic Web 8
    • 9. Current workDigital Enterprise Research Institute  Analysis of the corpus  Argument schemes (e.g. expert opinion)  Factors (e.g. notability, uniqueness)  Newcomer’s arguments  Interviews  Administrators  Experienced users  Argument exploration  Text mining cue words (‘however’, ‘therefore’,…)  Architecture  Ontology development  “Standpoints Web” 9
    • 10. StandpointDigital Enterprise Research Institute [Delete the article]...hasnt played since 2008. His 66-73 record is far from stellar and, in my opinion, does not merit an article. Proposition: does not merit an article Justification: hasn’t played since 2008, bad record 10
    • 11. Opposing standpointDigital Enterprise Research Institute >>He pitched last month and plays for the Venezuelan League. This meets our article criteria. Proposition: keep the article Justification: meets our article criteria 11
    • 12. Possible applicationsDigital Enterprise Research Institute  Visualize decision-making  Highlight controversies  Query opinions and arguments  Discuss arguments interactively with a bot  Calculate the “best” options  Analyze, extract, and represent disagreement 13
    • 13. Thanks!Digital Enterprise Research Institute @jschneider
    • 14. AcknowledgmentsDigital Enterprise Research Institute  Thanks to our collaborators!  Katie Atkinson, Trevor Bench-Capon, Adam Wyner (Liverpool)  DERI Social Software Unit  Rhetorical Structure, W3C Health Care and Life Sciences  Funding  Science Foundation Ireland Grant No. SFI/08/CE/I1380 (Líon-2)  Short-term scientific mission (STSM 1868) from the COST Action ICO801 on Agreement Technologies 15
    • 15. Digital Enterprise Research Institute
    • 16. “ELIZA for arguments”Digital Enterprise Research Institute Snaith, Lawrence, & Reed, “Mixed initiative argument in public deliberation,” ODET 2010 17
    • 17. Highlight ControversiesDigital Enterprise Research Institute Ennals, R., Trushkowsky, B., & Agosta, J. M. (2010). Highlighting Disputed Claims on the Web. In WICOW at WWW 2010.
    • 18. Transform Debates into Argument FrameworksDigital Enterprise Research Institute (1) Households should pay tax for their garbage. (4) (1) Paying tax for garbage increases recycling, so households should Arrow: premise pay. (3) (1) Wyner, van Engers, & Bahreini. Recycling more is good, so people From Policy-making Statements to First-order Logic. should pay tax for their garbage. EGOV 2010 19
    • 19. Calculate best options (non-contradictory opinions)Digital Enterprise Research Institute Wyner, van Engers, & Bahreini. From Policy-making Statements to First-order Logic. EGOV 2010 20
    • 20. Claims ExtractionDigital Enterprise Research Institute  Cue words (“Hence Jaffa Cakes are cakes.”) [Marcu]  Rhetorical Structure Theory
    • 21. Claims Extraction
    • 22. Important RelationshipsDigital Enterprise Research Institute  Attacks  Supports 23
    • 23. Case StudyDigital Enterprise Research Institute  Understand: interviews, observation, and content analysis  Intervene: Implement & test the Standpoints Web architecture on Wikipedia deletion discussions  Evaluation: Community feedback Ontology fitness-for-purpose Precision & recall? 25
    • 24. Digital Enterprise Research Institute  Problem  Possible Uses of a Knowledge Representation  Concrete Examples  Some Current Directions 26
    • 25. The ProblemDigital Enterprise Research Institute  The Web is full of opinions & commentary.  A lot of it disagrees.  How do we learn from other people, when they disagree? 27
    • 26. My ApproachDigital Enterprise Research Institute  Identify peoples’ views  Collect the explanations people give  Create a hypertext web of these views & explanations 28
    • 27. Two Persuasive MessagesDigital Enterprise Research Institute 29
    • 28. Why: Walton’s dialogue typesDigital Enterprise Research Institute 30
    • 29. Knowledge-based Claims VaryDigital Enterprise Research Institute  Use of statistics & impersonal information 31
    • 30. Versus personal appeals…Digital Enterprise Research Institute  Where opinions and personal values are explicit 32
    • 31. Why: Knowledge, Emotion, Valuesas a ProxyDigital Enterprise Research Institute 33
    • 32. Purpose-related keywordsDigital Enterprise Research Institute  Knowledge  statistics  Values  truth  secret  Rhetoric  you can thank  Judgment/Opinion  eradicate  tough  rejecting 34
    • 33. Purpose mattersDigital Enterprise Research Institute  Knowledge-oriented discussions are straightforward to reuse  Opinion-oriented discussion types may require caveating or balancing  emotion makes a discussion more interesting  can also indicate the potential for bias. 35
    • 34. Twitter: StandpointDigital Enterprise Research Institute Difference between cakes and biscuits? When stale, cakes go hard, biscuits go soft. Hence Jaffa Cakes are cakes. (Was official EU ruling). View: Jaffa Cakes are cakes Justification: official EU ruling; go hard when stale 36
    • 35. VisualizeDigital Enterprise Research Institute bCisive