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Ripples on the Web: Diffusion of Activity Bursts across Hyperlink Networks in Wikipedia
This document summarizes a study analyzing how bursts of activity diffuse across the hyperlink network on Wikipedia following major events, using the 2013 Boston Marathon bombings as a case study. The study finds that bursts of editing activity on articles are correlated with developments in the real world and that information seeking drives the creation of new articles and relationships between articles. Future work could further analyze how bursts diffuse through the larger hyperlink network and use textual features to predict bursts of activity.
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Ripples on the Web: Diffusion of Activity Bursts across Hyperlink Networks in Wikipedia
- 1. Ripples on theWeb: Diffusion of Activity Bursts across Hyperlink Networks in Wikipedia Brian Keegan (@bkeegan) Yu-Ru Lin (@rhodiuslin) David Lazer (@davidlazer) Sunbelt XXXIII Hamburg, Germany May 23, 2013
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- 3. Theoretical motivations • Informationseeking and sense-making • What kinds of information is general population seeking following disaster? • Mass convergence and crisis informatics • What implications does rapidly emerging information have for emergency responders? • Networks of knowledge and collaboration • How is this information verified and synthesized?
- 4. Twitter basically sucks •Information seeking and sense-making • More noise & echo than signal, fragmented behavior & commons • Mass convergence and crisis informatics • Sampling & temporal censoring • Networks of knowledge and collaboration • Unverifiable, misinformation, non- cumulative
- 5. Wikipedia basically rules •Information seeking and sense-making • Existing repertoires & activity around contextual information • Mass convergence and crisis informatics • Fine-grained & accessible history • Networks of knowledge and collaboration • Cited, debated, and cumulative account
- 6. Networks from Wikipediadata • Markup • Hyperlinks: i has a link to j • Revisions • Coauthorship: i shares an editor with j • Pageview activity • Correlation: i’s pageviews correlated with j
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- 8. Case study • BostonMarathon bombings • Two distinct dates for burst of activity related to major developments: • April 15: Bombing • April 19: Manhunt • New information new articles bursting
- 9. Article dynamics –First 3 weeks 9
- 10. Dynamics – First18 hours 10
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- 12. Pageview and editingcoupling
- 13. Pageview and editingcoupling
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- 15. Types of networks •Markup • Hyperlinks: i has a link to j • Revisions • Coauthorship: i shares an editor with j • Pageview activity • Correlation: i’s pageviews correlated with j
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- 18. Communities Perpetrators MIT PD Watertown, Mass. Shelterin place Holy Cross Pressure cooker
- 19. Burst detection 19 Rolling 30day average 2x SE
- 20. Largest bursts 1. Groundstop (329) 2. Boylston Street (268) 3. Google Person Finder (237) 4. Patriots’ Day (201) 5. Copley Square (171) 6. Controlled explosion (168) 7. Lenox Hotel (116) 8. Pressure cooker (83) 9. MA EMA (83) 10. BP SOU (78) 20
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- 24. April 18 MIT PD Watertown,Mass. Shelter in place
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- 29. Types of networks •Markup • Hyperlinks: i has a link to j • Revisions • Coauthorship: i shares an editor with j • Pageview activity • Correlation: i’s pageviews correlated with j
- 30. Coauthorship activity 4/15– 5/1 30
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- 32. Types of networks •Markup • Hyperlinks: i has a link to j • Revisions • Coauthorship: i shares an editor with j • Pageview activity • Correlation: i’s pageviews correlated with j
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- 34. 34 Duffel bag New York Times MGH Activitycorrelation network
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- 37. Theoretical framework • Informationseeking and sense-making • Fine-grained traces of large-scale behavior in a complex information space • Mass convergence and crisis informatics • Nearly real-time behavior captures bursts of activity related to current events • Networks of knowledge and collaboration • Information seeking in knowledge network drives creation of new knowledge and relationships
- 38. Future directions • Trackdiffusion of bursts across larger hyperlink network • Are distant bursty events responsible for substantial fraction of editing activity? • Synchronized and anomalous bursts of activity as narrative elements • Czech Republic vs. Chechnya • Classifying events and mobilizing resources
- 39. Future directions • Textualfeatures predict bursts? • Edit distance, number of mentions, position on page, etc. convey relatedness of content • Multilevel & longitudinal statistical model of tie formation • Dyadic covariates: Pageview correlation coauthorship ties hyperlinks
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