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Geolocation and Geographic Imaginaries ( or place framing as the “ becoming ” of networked space)

Geolocation and Geographic Imaginaries ( or place framing as the “ becoming ” of networked space). Josef Eckert University of Washington Feb. 24, 2012. Making sense of this…. from the flickr stream of _PaulS_. As it relates to this.

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Geolocation and Geographic Imaginaries ( or place framing as the “ becoming ” of networked space)

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  1. Geolocation and Geographic Imaginaries (or place framing as the “becoming” of networked space) Josef Eckert University of Washington Feb. 24, 2012

  2. Making sense of this…. from the flickr stream of _PaulS_

  3. As it relates to this. Occupy-related corpus, hex-binned (2dd), geolocated tweets, Oct. 19 – Dec. 31

  4. Full disclosure

  5. 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 socialmovements 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. Collection Method (AWS & Hadoop)

  7. 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. Seeding keywords: occupytogether.org Oct. 26 Number of Occupiers Reporting on Meetup.com 0 - 10 11 - 50 51 - 100 101 - 150 151 - 200 201 - 256

  9. Occupy-related corpus, hex-binned (.05 dd), geolocated tweets, Oct. 19 – Dec. 31

  10. New York Oakland Seattle 791 (0.02%) Clustered, p<.001 369 (46.65%) 199 (0.004%) Clustered, p<.001 95 (47.70%) Within cityANN Within 1000’ 1,480 (0.03%) Clustered, p<.001 549 (37.09%)

  11. 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. @notarealperson: “Elderly woman takes pepper spray to the face #ows#occupyseattle #occupypdx”

  13. Measuring great circle distance

  14. Hashtag co-occurrence great circles (unclassed), Oct. 19 – Dec. 31 ~6,700 links

  15. Bias of the geo-crowd(Zook & Graham 2010) = Biased data sampling

  16. A raw count of co-occurrence

  17. Linear regression to determine explanatory power of distance for hashtag co-occurrence. Not significant Not even close

  18. Jaccard Index 8 4 5 2 A ∩ B 6 A ∪ B

  19. Which looks like this…. Co-occurrence Co-occurrence Jaccard I Jaccard I Austin, TX <-> Houston, TX Memphis, TN <-> Milwaukee, WIAuburn, CA <-> Huntsville, AL*Los Angeles, CA <-> Oakland, CAAnn Arbor, MI <-> Lansing, MI* 0.3630.2770.2220.1970.188 Colorado Springs, CO <-> Ft. Collins, COBoulder, CO <-> Denver, COAtlanta, GA <-> Oakland, CADallas, TX <-> Houston, TXBoulder, CO <-> Ft. Collins, CO 0.1280.1280.1270.1250.117

  20. Linear regression to determine explanatory power of distance for the Jaccard Index of hashtagco-occurrence. Not significant Not even close

  21. 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. So…what next? • Blockmodeling – attempting to consider multiplesimilarities 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. #Oct20 sukey.io

  24. 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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