Where in the world is Edward Snowden? Alex / Aron / Karin / Sam

Theory: Three Propositions First, that we recognize space as the product of interrelations; as constituted through interactions. Second, that we understand space as the sphere of the possibility of the existence of multiplicity. Third, that we recognize space as always under construction. - Doreen Massey, For Space (2005, 9)

Interest: (How) Can we use Twitter data and DMI tools to interrogate space(s) as the product of interrelations, and as always under construction? Aim: To open up the restrictive nature of the Snowden discourse so far, as epitomized in the Guardian map.

Research Questions RQ 1 - What geographic locations have been dominant in the twitter discourses around Edward Snowden over time between June 19 th and June 26 th ? RQ 2 - What terms were associated with these locations in the Twitter discourse around Snowden on these dates? RQ 3 - What shifts can thus be identified in locational discourses surrounding Snowden ‘s case?

Research method 1.Querying ‘SNOWDEN’ within Prism dataset, for each day. 2.Extracting 10k random tweets per set. 3.Stripping stop-words and visualizing the remaining body of text via the Tag-cloud tool. 4.Locating top three locations mentioned as key themes per day,

Research method – contd. 4.Querying these location terms in the periods in which they were dominant for co-hashtag analysis. 5. Utilizing Gephi to explore the network of themes bundled with each location.

Fugitive Geographies– Iceland ‘Global Leakers Unite’: Assange, Wikileaks, Asylum, Whistle-blower.

Fugitive Geographies – HK Speculations: Moscow, Russia, China, Cuba, Venezuela. Surveillance/ Whistleblower moving away.

Fugitive Geographies – Moscow Transit Limbo: hollidayinnfullofspies-hotel-motel, extradition-wikileaks-whistleblowers, Putin-Russia.

Challenges DM Literacy. Dataset issues: –Framed by the predefined DMI search terms. –Outages. –Secondary script drops (04:00). Tools: –Geo-extractor and OpenCALAIS didn’t work: identify non-place terms as places. –Co-hashtag analysis instead of co-word analysis. Visualization –Reoccurrence of overly dominant hashtags (#NSA, #Snowden, #prism) – deleted. –Temporal visualization.

Conclusion Our aim was to open up the restrictive nature of the Snowden discourse so far, as epitomized in the Guardian map. To explicate the spatial heterogeneity in line with Massey’s (2005) propositions. To interrogate Twitter data and news articles in order to show this contested terrain. To suggest that the shift from a surveillance narrative to a focus on the fugitive status of Snowden was never certain, fixed or inevitable.

Q&A