Envisioning a discussion dashboard for collective intelligence of web conversations...

Copyright 2010 Digital Enterprise Research Institute. All rights reserved.

Digital Enterprise Research Institute

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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


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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

mailto:[email protected]

http://jodischneider.com/jodi.html


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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

Envisioning a discussion dashboard for collective intelligence of web conversations...

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