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Occupied Geographies Relational and Otherwise

Occupied Geographies Relational and Otherwise. Josef Eckert, Department of Geography. Jeff Hemsley , Information School. University of Washington. April 11, 2013. Occupy Wall Street. Included both digital and urban spaces Localized, networked processes New social media tactics.

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Occupied Geographies Relational and Otherwise

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  1. Occupied Geographies Relational and Otherwise Josef Eckert, Department of Geography Jeff Hemsley, Information School University of Washington April 11, 2013

  2. Occupy Wall Street • Included both digitaland urban spaces • Localized, networkedprocesses • New social mediatactics

  3. What role does place play within network structures of Twitter? Are actors both in place and on Twitter interacting with one another?

  4. Motivation Twitter and Social Network Analysis seem to be trending right now #overlyhonestmethods

  5. Motivation • Urban processes are lived experiences(Lefebvre) • These experiences are digitally mediated(Crampton, Leszczynski) • The digital is inextricably part of urban life(Kitchen, Dodge, Zook, Graham)

  6. Motivation • Twitter as a tactic for organization and protest (Gerbaudo) • Decentralized, networked organizations of protest (Castells) • A geographic focus on networks and the role they play in contentious politics (Leitner et. al, Nicholls) • Moving beyond the geotag as a unit of place (Crampton et. al)

  7. A first cut at exploration…. Testing a “common sense” assumption: Are protesters that represent themselves as living in places with protest locations more likely to interact (@-mention) with others that also represent themselves as living in that place?

  8. Data Gathering • 10/19/2011 – current day • 300k – 1m tweets per day,215 “keyterms” • Gathered using streaming(now REST) API • Have to slice the data

  9. That’s SoMe Toolkit!

  10. Data Preparation Six hashtags representative of protest locations: #occupyslc, #occupyportland, #occupyseattle, #occupyhouston, #occupydenver, and #occupyorlando(and #ows for fun) Reduced dataset to only those users with both in- and out-going @-mention links (those interactingbi-directionally) Temporally bounded: 7-day (10/19/2011 – 10/25/2011) 30-day (10/19/2011 – 11/19/2011) #ows: 1-day (10/19/2011) – my computer is melting!

  11. Data Preparation: Users “in Place” Avoiding geotagging, attempting to use user-defined location Obtained a list of user-defined places for users participating in a hashtag – checking for alternative city matches (“SLC”) Used Regular Expression matching to determine if a user was “in place” for a given hashtag (e.g. “Salt Lake | Salt Lake City | SLC”)

  12. Orlando, 30 days

  13. Denver, 30 days

  14. OWS, *1* day

  15. Step 1: QAP Testing (Quadradic Assignment Problem) • QAP Testing Matrices • QAP uses random Monte Carlo iterations rather than inference metrics • Tests against three null hypotheses: • x1: Users with mutual ties do not @-mention • one another in a way that significantly differs • from a random distribution • x2: Users in a mutual place do not … • x3: Users with more followers are not @-mentioned… Jeff Joe Shawn Jeff 0 1 0 Joe 0 0 1 0 Shawn 0 0

  16. Step 2: Fit QAP coefficents to OLS Regression EYij = β0 + β1X1ij+ β2X2ij+ β3X3ij DV: Users “inPlace” Matrix DV: Mutual TieMatrix DV: Follower Count Matrix Intercept IV: Matrix, # of @-mentions

  17. Insignificance is more interesting….

  18. There’s still much to do • The model fit could be better • Analysis across multiple temporal slices • Application to the other 154 locationalhashtags • Continued sensitivity testing to confirm that “place matters” in social media network construction. But how?

  19. Future Directions future directions in visualization Cliques & Topic Modeling portland network, portland super-clique vignette Portland, 7 days

  20. Thank you! Questions and Suggestions? This research was made possible by: NSF Award #1243170 INSPIRE: Tools, Models, and Innovation Platforms for Research on Social Media

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