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"Visual analysis of controversy in user-generated encyclopedias": A critical summary
 

"Visual analysis of controversy in user-generated encyclopedias": A critical summary

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This is a critical summary of an article by Ulrik Brandes and Jurgenv Lerner from their article in Information Visualization, volume 7, issue 1

This is a critical summary of an article by Ulrik Brandes and Jurgenv Lerner from their article in Information Visualization, volume 7, issue 1

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    "Visual analysis of controversy in user-generated encyclopedias": A critical summary "Visual analysis of controversy in user-generated encyclopedias": A critical summary Presentation Transcript

    • Summarizing Visual analysis of controversy in user-generated encyclopedias by Brandes, U., & Lerner, J. (2008). Information Visualization, 7(1), 34-48. Diego Maranan IAT 814 22 October 2009
    • About the article • In a nutshell: contributing to the understanding of authorship dynamics in collaboratively-edited articles on controversial topics • 16 pages • 18 citations on Google Scholar • University of Konstanz, Germany • Social network analysis and graph layouts since 1999
    • Article contents Why this paper matters 693 words What’s been done before 635 words Their unique contribution 228 words How they acquire the data 307 words How they parse and filter the data 121 words How they mine and represent the data 3757 words How they make the visualization interactive 362 words Examples 724 words Conclusion 329 words
    • Why this matters • Wikipedia: reliability depends on its “neutrality” • Neutrality depends on dynamics of author community – Who contributes? – What are the disputes? – What are the roles of the authors? • Roles – Agree or disagree – Regular or sporadic – Contribute a lot or a little – Revise or be revised (“Reactionary” or “revolutionary”)
    • What’s been done before • Wikipedia research – Disagreement based on the number of reverts (Kittur et al 2007) • NLP-based research on Web 2.0 applications – Analysis of polarizing factors in user opinions (aka people like The Da Vinci Code for different reasons than they hate it) (Chen et al 2006) – Identifying polarizing language and partitioning into opinion groups (Nigam and Hurst 2004) – Metrics for polarity + buzz + author dispersion (Glance et al 2005) • Non-NLP – Vocabulary used by opponent sides tend to be largely identical (Han & Kamber 2006) – Links carry less noisy information than text (Agrawal et al 2003) – Simpler • Visualization-related – Force-directed layout (equally-spacing nodes) – Multidimensional scaling
    • Finished product: "Who-revises-whom"-network in 2D space
    • Legend Opposition (to another node) Distance from other node Thickness of edge connecting to other node Involvement (number of edits made) Node area Number and aggregate thickness of edges Role (revisor vs. revisee) Luminance of edge (in relation to a particular author) Shape (in relation to all authors in general) Variance in edit frequency Node brightness
    • Ingredients: Wikipedia XML dump of revision history file This visualization uses the following revision data: • Timestamp • Author name (or IP address) • A flag to denote whether a revision was a revert … that's it.
    • How to get from XML dump to network visualization? Three major problems • Inferring who revises who (data mining issue) • Obtaining a metric for conflict between authors (data mining issue) • Meaningfully mapping the authors into a 2D network based on conflict (visual representation issue) Remaining problems are relatively easy • Visually coding individual involvement and participation variance • Visually coding conflict • Presenting aggregate involvement (bar chart) • Choosing and designing interactive features
    • Problem 1: Inferring who revises who • You are probably revising a revision immediately previous to yours if – A short amount of time has passed since the latest revision – You revise several times in a row – Your revision is a revert • Example: Alice and Bob
    • Problem 1: Inferring who revises who Therefore… • Assumptions: Authors revise in a timely manner revisions they're interested in • Does not detect revisions where revisor is revising old content (Assumption: these are a minority compared to intensely conflictual revisions, which happen very quickly) • The probability that revision by author u following revision made by author v can be interpreted as "u is revising v" is approximated by
    • Problem 2: Obtaining a metric of conflict between authors Agrawal et al (2003) • Quoting an author from a previous usenet post creates a quotation link between authors (Link => relationship) • Quotation link implies disagreement in controversies ("You said such-and- such. Well, here's what I think.") • Limited applicability; “assumes a single topic per posting and poster is ‘for’ or ‘against’ that standpoint” (Nigam)
    • Problem 2: Obtaining a metric for conflict between authors Therefore… • Authors revise only what they don't agree with. (They don't revise to support a statement.) • A revision between authors is a "vote" for conflict between authors • The degree of conflict duv for authors u, v is given by
    • Problem 3: Mapping authors to a 2D space A celebration of matrix math Symmetric adjacency matrix. (duv = dvu) Let xu and xv be the x-coordinate of authors u and v respectively. Therefore, if u and v are in conflict, xuxvduv is large and negative.
    • Problem 3: Mapping authors to a 2D space Solve for all x by minimizing the sum How? Find the smallest eigenvalue, λmin, associated with A. The associated eigenvector has all the x-coordinates. Solve this: Claim: results optimized arrangement of authors along x-axis. Do the same for y-axis by taking second smallest eigenvalue, λ'min
    • Problem 3: Mapping authors to a 2D space Results Authors with largest duv entry are furthest away from each other x Two unrelated bipolar conflicts are separated along x and y axes. (They prove this for the general case.) y x
    • Problem 3: Mapping authors to a 2D space Scaling and tripolar conflicts "Real arguments are not quite so polarized," says authors. Degrees of tripolar conflict Claim: scale the y-values by λmin /λ'min ... really?
    • Problem 3: Mapping authors to a 2D space Conflict 2 axis Conflict 1 axis 1 set of opposing 2 sets of opposing What does this mean? opinions opinions How do you represent 3-polar Ex: Ripe vs unripe (conflict 1 is conflicts in a Cartesian bananas independent of presentation space, which conflict 2) inherently employs greater-than and less-than ordering? Ex: Ripe vs unripe Ex: bananas Bananas vs apples vs oranges & Green vs red apples
    • Problem 3: Mapping authors to a 2D space Explanation about scaling makes more sense after normalization around ellipse. Place all the points around an ellipse by "exploding" them from the center.
    • Legend Opposition (to another node) Distance from other node Thickness of edge connecting to other node Involvement (number of edits made) Node area Number and aggregate thickness of edges Role (revisor vs. revisee) Luminance of edge (in relation to a particular author) Shape (in relation to all authors in general) Variance in edit frequency Node brightness
    • Filtering by time intervals and by number of edges shown
    • Contributions Extends multidimensional scaling by visualizing independent conflict(s) (2 bipolar conflicts) Builds on the work by Agrawal and Kittur Some limitations Only a representation; ignores lesser conflicts (but highlights important ones) Major assumptions about nature of content based on limited data! Very specific application Extends the work of Kittur et al to include non-revert based disagreements Some criticisms Question about meaning of the 2D space. "Absolute value of x and y coordinate indicates involvement in the conflict" Illustrations don’t match the description in the text
    • Question for you How do you represent multipolar conflict in general in 2D? (Thanks.)