Occupied Geographies Relational and Otherwise

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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

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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. - PowerPoint PPT Presentation

Transcript of Occupied Geographies Relational and Otherwise

Page 1: Occupied Geographies Relational and Otherwise

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

Page 2: Occupied Geographies Relational and Otherwise

SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013

Occupy Wall Street

• Included both digitaland urban spaces

• Localized, networkedprocesses

• New social mediatactics

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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?

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SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013

Motivation

Twitter and Social Network Analysis seem to be trending right now

#overlyhonestmethods

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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)

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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)

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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?

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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

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SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013

That’s SoMe Toolkit!

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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!

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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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SoMe LabSocial Media Lab @ UW

Orlando, 30 days

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SoMe LabSocial Media Lab @ UW

Denver, 30 days

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SoMe LabSocial Media Lab @ UW

OWS, *1* day

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• 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

Jeff

Joe

Shawn

Shawn

1

1

0 0

0 0

000

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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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SoMe LabSocial Media Lab @ UW @somelabresearch@joeeckert #aag2013

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….

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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?

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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

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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?