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SoMe Lab Social Media UW #aag2013 Occupied Geographies Relational and Otherwise Josef Eckert, Department of Geography.

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Presentation on theme: "SoMe Lab Social Media UW #aag2013 Occupied Geographies Relational and Otherwise Josef Eckert, Department of Geography."— Presentation transcript:

1 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 Occupied Geographies Relational and Otherwise Josef Eckert, Department of Geography Jeff Hemsley, Information School University of Washington April 11, 2013

2 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 Occupy Wall Street Included both digital and urban spaces Localized, networked processes New social media tactics

3 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 What role does place play within network structures of Twitter? Are actors both in place and on Twitter interacting with one another?

4 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 Motivation Twitter and Social Network Analysis seem to be trending right now #overlyhonestmethods

5 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 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 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 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 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 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 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 Data Gathering 10/19/2011 – current day Gathered using streaming (now REST) API 300k – 1m tweets per day, 215 “keyterms” Have to slice the data

9 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 That’s SoMe Toolkit!

10 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 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 interacting bi-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 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 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 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 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… Step 1: QAP Testing (Quadradic Assignment Problem) JeffJoe Jeff Joe Shawn 1 1 0 0 00 0 00

16 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 EY ij = β 0 + β 1 X 1ij + β 2 X 2ij + β 3 X 3ij IV: Matrix, # of @-mentions Intercept DV: Mutual Tie Matrix DV: Users “in Place” Matrix DV: Follower Count Matrix Step 2: Fit QAP coefficents to OLS Regression

17 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 X 1 (mutual tie)X 2 ("In Place")X 3 (Receiver's Followers)Adjusted R 2 Orlando 7-day0.0000.0180.1100.2608 Orlando 30-day0.000 0.6180.2807 Houston 7-day0.0000.0180.2580.4028 Houston 30-day0.000 0.395 Salt Lake City 7-day0.0000.0640.8620.3775 Salt Lake City 30-day0.0000.0020.0180.1998 Seattle 7-dayXXXX Seattle 30-day0.0000.0010.0060.1629 Denver 7-day0.0000.4240.0240.2665 Denver 30-day0.000 0.0040.1516 Portland 7-day0.0000.7660.4160.4472 Portland 30-day0.000 0.0080.1267 OWS 1-day0.000 0.1572 Insignificance is more interesting….

18 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 There’s still much to do The model fit could be better Analysis across multiple temporal slices Application to the other 154 locational hashtags Continued sensitivity testing to confirm that “place matters” in social media network construction. But how?

19 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 future directions in visualization portland network, portland super-clique vignette Future Directions Portland, 7 days Cliques & Topic Modeling

20 SoMe Lab Social Media Lab @ UW @somelabresearch @joeeckert #aag2013 This research was made possible by: NSF Award #1243170 INSPIRE: Tools, Models, and Innovation Platforms for Research on Social Media Thank you! Questions and Suggestions?


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