SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
Occupied GeographiesRelational and Otherwise
Josef Eckert, Department of GeographyJeff Hemsley, Information School
University of Washington
April 11, 2013
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
Occupy Wall Street
• Included both digitaland urban spaces
• Localized, networkedprocesses
• New social mediatactics
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
What role does place play within network structures of Twitter?
Are actors both in place and on Twitter interactingwith one another?
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
Motivation
Twitter and Social Network Analysis seem to be trending right now
#overlyhonestmethods
SoMe LabSocial 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)
SoMe LabSocial 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)
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
A first cut at exploration….
Testing a “common sense” assumption:
Are protesters that represent themselves as livingin places with protest locations more likely to interact (@-mention) with others that also represent themselvesas living in that place?
SoMe LabSocial 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
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
That’s SoMe Toolkit!
SoMe LabSocial 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- andout-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!
SoMe LabSocial 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 participatingin 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”)
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Orlando, 30 days
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Denver, 30 days
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OWS, *1* day
SoMe LabSocial 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 @-mentionone another in a way that significantly differsfrom 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)
Jeff Joe
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Shawn
Shawn
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1
0 0
0 0
000
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
EYij = β0 + β1X1ij + β2X2ij + β3X3ij
IV: Matrix, # of @-mentions
Intercept
DV: Mutual TieMatrix
DV: Users “inPlace” Matrix
DV: FollowerCount Matrix
Step 2: Fit QAP coefficents to OLS Regression
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X1 (mutual tie) X2 ("In Place") X3 (Receiver's Followers) Adjusted R2
Orlando 7-day 0.000 0.018 0.110 0.2608Orlando 30-day 0.000 0.000 0.618 0.2807Houston 7-day 0.000 0.018 0.258 0.4028Houston 30-day 0.000 0.000 0.000 0.395Salt Lake City 7-day 0.000 0.064 0.862 0.3775Salt Lake City 30-day 0.000 0.002 0.018 0.1998Seattle 7-day XXXX XXXX XXXX XXXXSeattle 30-day 0.000 0.001 0.006 0.1629Denver 7-day 0.000 0.424 0.024 0.2665Denver 30-day 0.000 0.000 0.004 0.1516Portland 7-day 0.000 0.766 0.416 0.4472Portland 30-day 0.000 0.000 0.008 0.1267OWS 1-day 0.000 0.000 0.000 0.1572
Insignificance is more interesting….
SoMe LabSocial 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 locationalhashtags
• Continued sensitivity testing to confirm that “place matters” in social media network construction. But how?
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
future directions in visualization
portland network, portland super-clique vignette
Future Directions
Portland, 7 days
Cliques &Topic Modeling
SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013
This research was made possible by:NSF Award #1243170INSPIRE: Tools, Models, and Innovation Platforms for Research on Social Media
Thank you! Questions and Suggestions?