Copyright 2010 Digital Enterprise Research Institute. All rights reserved.
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Envisioning a Discussion Dashboard for Collective
Intelligence of Web Conversations
Jodi Schneider and Alexandre Passant
Collective Intelligence as Community Discourse and Action at CSCW 20122012-02-11Seattle, Washington
Digital Enterprise Research Institute
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Discussions are ubiquitous…
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Discussions are ubiquitous…
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Discussions are ubiquitous…
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Challenges
1. Discussion isn’t limited to a single platform, discussion space, or argumentation tool.
2. How do we identify & summarize disagreement on the Web?
3. Can we highlight the points of contention? Minority opinions?
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Dashboard goals
Orient readers to discussions. Spot juicy/tough parts automatically. Provide a framework for integrating with
manual tools for analysis & sensemaking.
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5W’s – a simple, familiar model
Who What When Where Why
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Two Persuasive Messages
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Why: Walton’s dialogue types
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Knowledge-based Claims Vary
Use of statistics & impersonal information
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Versus personal appeals…
Where opinions and personal values are explicit
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Why: Knowledge, Emotion, Values as a Proxy
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Purpose-related keywords
Knowledge statistics
Values truth secret
Rhetoric you can thank
Judgment/Opinion eradicate tough rejecting
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Purpose matters
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.
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Thanks!
[email protected]://jodischneider.com/jodi.html@jschneider
Digital Enterprise Research Institute
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Acknowledgments
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
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Who
Reply networks Demographic statistics Organization, affiliation, …
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Who
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What
Hashtags Topic drift & variation
Word frequency
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When
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
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Where
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?
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Genre & source matters
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Context to combine these?
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What are the turning points in a discussion?
Which viewpoints have diverse support? What justifications are given for
viewpoints?
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Why
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Standpoints Web
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Standpoints Web
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Transform Debates into Argument Frameworks
(1) Households should pay tax for their garbage.
(4) (1) Paying tax for garbage increases recycling, so households should pay.
(3) (1)Recycling more is good, so people should pay tax for their garbage.
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Arrow: premise
Wyner, van Engers, & Bahreini. From Policy-making Statements to First-order Logic. EGOV 2010
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Calculate best options (non-contradictory opinions)
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Wyner, van Engers, & Bahreini. From Policy-making Statements to First-order Logic.
EGOV 2010
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