1. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Geolocation and Geographic Imaginaries (or place framing as the “becoming” of networked space) Josef Eckert University of Washington Feb. 24, 2012

2. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Making sense of this…. from the flickr stream of _PaulS_

3. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW As it relates to this. Occupy-related corpus, hex-binned (2dd), geolocated tweets, Oct. 19 – Dec. 31

4. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Full disclosure

5. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW The fuzziness of physical, relational, and networked space Protest as contributing to the “becoming” of space, negotiating “the right to the city” (Lefevbre, Mitchell 2003) “Place-framing as place-making” (Martin 2003) The expanding role of technology and networks for social movements and resistance within space (Nicholls 2008 a,b ) Tobler’s Law (1970): “Everything is related to everything else, but near things are more related than far things”

6. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Collection Method (AWS & Hadoop)

7. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW The Baleen Whale Approach to Data Gathering Twitter is ephemeral 200 million tweets per day 178 related search terms via Twitter API ~300,000 – 1,000,000 tweets per day collected “The edges of these bones are fringed with hairy fibres, through which the Right Whale strains water, and in those intricacies he retains the small fish, when open-mouthed he goes through the seas of brit in feeding time.” (Melville 1851)

8. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Seeding keywords: occupytogether.org 0 - 10 11 - 50 51 - 100 101 - 150 151 - 200 201 - 256 Number of Occupiers Reporting on Meetup.com Oct. 26

9. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Occupy-related corpus, hex-binned (.05 dd), geolocated tweets, Oct. 19 – Dec. 31

10. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW New YorkOaklandSeattle 1,480 (0.03%) Clustered, p<.001 549 (37.09%) 791 (0.02%) Clustered, p<.001 369 (46.65%) 199 (0.004%) Clustered, p<.001 95 (47.70%) Within city ANN Within 1000 ’

11. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW But that’s not a lot of tweets! Is there any other way we can read geographies from Twitter activity? Is 20,645,921 tweets enough of a start?

12. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW @notarealperson: “Elderly woman takes pepper spray to the face #ows #occupyseattle #occupypdx”

13. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Measuring great circle distance

14. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Hashtag co-occurrence great circles (unclassed), Oct. 19 – Dec. 31 ~6,700 links

15. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Bias of the geo-crowd (Zook & Graham 2010) = Biased data sampling

16. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW A raw count of co-occurrence

17. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Not significant Linear regression to determine explanatory power of distance for hashtag co-occurrence. Not even close

18. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW 6 5 4 2 8 Jaccard Index A ∩ B A ∪ B

19. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Which looks like this…. Austin, TX Houston, TX Memphis, TN Milwaukee, WI Auburn, CA Huntsville, AL* Los Angeles, CA Oakland, CA Ann Arbor, MI Lansing, MI* 0.363 0.277 0.222 0.197 0.188 Colorado Springs, CO Ft. Collins, CO Boulder, CO Denver, CO Atlanta, GA Oakland, CA Dallas, TX Houston, TX Boulder, CO Ft. Collins, CO 0.128 0.128 0.127 0.125 0.117 Co-occurrence Jaccard I Co-occurrence Jaccard I

20. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Not significant Linear regression to determine explanatory power of distance for the Jaccard Index of hashtag co-occurrence. Not even close

21. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW Not quite a conclusion, but we still learned something! Following standard geospatial statistic methods winds up dropping out a lot of data – don’t let the whale impress you. The ephemeral nature of Twitter data poses methodological risks for emergent events User-generated content (in this instance) again follows known patterns of geographic bias Distance is likely more than Cartesian; but distance is also not likely to be solely relative. And the local/global scale remains relevant – GEOGRAPHY MATTERS! (yay!)

22. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW So…what next? Blockmodeling – attempting to consider multiple similarities between cities: urban/rural, demographics, Jaccard Index, including inputs from SNA analysis from team members Qualitative interviews that attempt to get at some of the reasons folks think they’re chaining related hashtags

23. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW #Oct20 sukey.io

24. @joeeckert @somelabresearch SoMe Lab Social Medial Lab @ UW 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?