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

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



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: http://findicons.com/icon/27954/girl_5?id=27964# http://findicons.com/icon/27930/boy_8?id=27939# http://findicons.com/icon/27955/girl_4?id=27965#
  • 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

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

  • Digital Enterprise Research Institute www.deri.ie 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.
  • What I’m looking forDigital Enterprise Research Institute www.deri.ie  Scoping & focus  Detailed mentoring on CSCW/HCC methodologies  Interviewing  Qualitative Research  Statistics  Suggestions for evaluating my work 2
  • Digital Enterprise Research Institute www.deri.ie 3
  • Should we delete this article?Digital Enterprise Research Institute www.deri.ie 4
  • Improving deletion discussionsDigital Enterprise Research Institute www.deri.ie  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
  • Deletion argumentDigital Enterprise Research Institute www.deri.ie [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
  • GoalsDigital Enterprise Research Institute www.deri.ie  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
  • OverviewDigital Enterprise Research Institute www.deri.ie  Corpus:  All Wikipedia deletion discussions from January 29, 2011  Perspectives/approaches:  Argumentation  CSCW/HCC  Text analytics  Ontologies/Social Semantic Web 8
  • Current workDigital Enterprise Research Institute www.deri.ie  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
  • StandpointDigital Enterprise Research Institute www.deri.ie [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
  • Opposing standpointDigital Enterprise Research Institute www.deri.ie >>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
  • Possible applicationsDigital Enterprise Research Institute www.deri.ie  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
  • Thanks!Digital Enterprise Research Institute www.deri.ie jodi.schneider@deri.org http://jodischneider.com/jodi.html @jschneider
  • AcknowledgmentsDigital Enterprise Research Institute www.deri.ie  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
  • Digital Enterprise Research Institute www.deri.ie
  • “ELIZA for arguments”Digital Enterprise Research Institute www.deri.ie Snaith, Lawrence, & Reed, “Mixed initiative argument in public deliberation,” ODET 2010 17
  • Highlight ControversiesDigital Enterprise Research Institute www.deri.ie Ennals, R., Trushkowsky, B., & Agosta, J. M. (2010). Highlighting Disputed Claims on the Web. In WICOW at WWW 2010.
  • Transform Debates into Argument FrameworksDigital Enterprise Research Institute www.deri.ie (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
  • Calculate best options (non-contradictory opinions)Digital Enterprise Research Institute www.deri.ie Wyner, van Engers, & Bahreini. From Policy-making Statements to First-order Logic. EGOV 2010 20
  • Claims ExtractionDigital Enterprise Research Institute www.deri.ie  Cue words (“Hence Jaffa Cakes are cakes.”) [Marcu]  Rhetorical Structure Theory
  • Claims Extraction
  • Important RelationshipsDigital Enterprise Research Institute www.deri.ie  Attacks  Supports 23
  • Case StudyDigital Enterprise Research Institute www.deri.ie  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
  • Digital Enterprise Research Institute www.deri.ie  Problem  Possible Uses of a Knowledge Representation  Concrete Examples  Some Current Directions 26
  • The ProblemDigital Enterprise Research Institute www.deri.ie  The Web is full of opinions & commentary.  A lot of it disagrees.  How do we learn from other people, when they disagree? 27
  • My ApproachDigital Enterprise Research Institute www.deri.ie  Identify peoples’ views  Collect the explanations people give  Create a hypertext web of these views & explanations 28
  • Two Persuasive MessagesDigital Enterprise Research Institute www.deri.ie 29
  • Why: Walton’s dialogue typesDigital Enterprise Research Institute www.deri.ie 30
  • Knowledge-based Claims VaryDigital Enterprise Research Institute www.deri.ie  Use of statistics & impersonal information 31
  • Versus personal appeals…Digital Enterprise Research Institute www.deri.ie  Where opinions and personal values are explicit 32
  • Why: Knowledge, Emotion, Valuesas a ProxyDigital Enterprise Research Institute www.deri.ie 33
  • Purpose-related keywordsDigital Enterprise Research Institute www.deri.ie  Knowledge  statistics  Values  truth  secret  Rhetoric  you can thank  Judgment/Opinion  eradicate  tough  rejecting 34
  • Purpose mattersDigital Enterprise Research Institute www.deri.ie  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
  • Twitter: StandpointDigital Enterprise Research Institute www.deri.ie 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
  • VisualizeDigital Enterprise Research Institute www.deri.ie bCisive