Envisioning a discussion dashboard for collective intelligence of web conversations -cscw2012-collective-intelligence
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Envisioning a discussion dashboard for collective intelligence of web conversations -cscw2012-collective-intelligence

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Can we use Walton's discussion types to provide context for discussions? ...

Can we use Walton's discussion types to provide context for discussions?

Presentation for a CSCW2012 workshop on Collective Intelligence as Community Discourse and Action, focusing on the *why* of discussions, and detecting that with textmining.

See the position paper, Envisioning A Discussion Dashboard for Collective Intelligence of Web Conversations, at
http://events.kmi.open.ac.uk/cscw-ci2012/wp-content/uploads/2012/02/SchneiderPassant-cscw12-w33.pdf

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  • http://events.kmi.open.ac.uk/cscw-ci2012/
  • For instance, can we automatically detect discussions with high levels of disagreement? Can we understand the minority views? Either to persuade them to join the majority, or to find the wisdom in their alternate points of view? Can we identify explanations and justifications? “ The minority is always right.” - Henrik Ibsen
  • Highlightingg variance to Identify high variance and minority opinions. Bring summaries together. Show variance and minority opinions. Detect explanations for beliefs. Develop a framework for integrating machine and hand-summaries. Look at argumentation and persuasion structures.
  • Conceptually simple Salient
  • 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
  • Adding funding/collaborators slide.
  • attach statements to the people and organizations Avoid bias show the reply networks between pairs of people, perhaps indicating authors with icons or avatars.
  • Forum and listserv posts often rely on the surrounding messages for coherence and context. Temporal aspects of a discussion are also related to the genre; [11] observed that listservs have short, intense exchanges, organized as a tree, while blogs promote slower diffusion and may have multiple ancestor posts. Thus the source genre is relevant when combining messages from different genres, and in some cases messages may not be understandable without the context of the other messages to which they reply. Geographic information may serve a purpose in serious discussions, for instance, in contentious discussions about placenames; since naming conventions tend to be heavily correlated with geographic ties [27], indications about such ties can provide context for moderators about who is taking part in the discussion.
  • Web source: Combining messages from different genres must take the source into account. Time, tree, context.
  • 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

Envisioning a discussion dashboard for collective intelligence of web conversations -cscw2012-collective-intelligence Envisioning a discussion dashboard for collective intelligence of web conversations -cscw2012-collective-intelligence Presentation Transcript

  • Digital Enterprise Research Institute www.deri.ie Envisioning a Discussion Dashboard for Collective Intelligence of Web Conversations Jodi Schneider and Alexandre Passant Collective Intelligence as Community Discourse and Action at CSCW 2012 2012-02-11 Seattle, Washington© Copyright 2010 Digital Enterprise Research Institute. All rights reserved.
  • Discussions are ubiquitous…Digital Enterprise Research Institute www.deri.ie 2
  • Discussions are ubiquitous…Digital Enterprise Research Institute www.deri.ie 3
  • Discussions are ubiquitous…Digital Enterprise Research Institute www.deri.ie 4
  • ChallengesDigital Enterprise Research Institute www.deri.ie n Discussion isn’t limited to a single platform, discussion space, or argumentation tool. n How do we identify & summarize disagreement on the Web? n Can we highlight the points of contention? Minority opinions? 5
  • Dashboard goalsDigital Enterprise Research Institute www.deri.ie  Orient readers to discussions.  Spot juicy/tough parts automatically.  Provide a framework for integrating with manual tools for analysis & sensemaking. 6
  • 5W’s – a simple, familiar modelDigital Enterprise Research Institute www.deri.ie  Who  What  When  Where  Why 7
  • Two Persuasive MessagesDigital Enterprise Research Institute www.deri.ie 8
  • Why: Walton’s dialogue typesDigital Enterprise Research Institute www.deri.ie 9
  • Knowledge-based Claims VaryDigital Enterprise Research Institute www.deri.ie  Use of statistics & impersonal information 10
  • Versus personal appeals…Digital Enterprise Research Institute www.deri.ie  Where opinions and personal values are explicit 11
  • Why: Knowledge, Emotion, Valuesas a ProxyDigital Enterprise Research Institute www.deri.ie 12
  • Purpose-related keywordsDigital Enterprise Research Institute www.deri.ie  Knowledge  statistics  Values  truth  secret  Rhetoric  you can thank  Judgment/Opinion  eradicate  tough  rejecting 13
  • 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. 14
  • 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 16
  • Digital Enterprise Research Institute www.deri.ie 17
  • WhoDigital Enterprise Research Institute www.deri.ie  Reply networks  Demographic statistics  Organization, affiliation, … 18
  • WhoDigital Enterprise Research Institute www.deri.ie 19
  • 20
  • WhatDigital Enterprise Research Institute www.deri.ie  Hashtags  Topic drift & variation  Word frequency 21
  • WhenDigital Enterprise Research Institute www.deri.ie  Message order  Out of date  Superceded  ‘First mover advantage’  ‘Last word’  Timelines  Depth of the reply network  Tends to indicate what has been most heavily discussed 22
  • WhereDigital Enterprise Research Institute www.deri.ie  Geographic information  Assumed viewpoints  Likely biases  Genre and source  How big are individual messages?  Do they stand on their own? Require reply context?  How fast does the conversation move? 23
  • Genre & source mattersDigital Enterprise Research Institute www.deri.ie 24
  • Context to combine these?Digital Enterprise Research Institute www.deri.ie 25
  • Digital Enterprise Research Institute www.deri.ie  What are the turning points in a discussion?  Which viewpoints have diverse support?  What justifications are given for viewpoints? 26
  • WhyDigital Enterprise Research Institute www.deri.ie 27
  • Standpoints WebDigital Enterprise Research Institute www.deri.ie 28
  • Standpoints WebDigital Enterprise Research Institute www.deri.ie 29
  • 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 30
  • 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 31