from geographic location to network location: the potential of big social data

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From Geographic Location to Network Location: The Potential of Big Social Data Prof. Axel Bruns ARC Future Fellow Digital Media Research Centre Queensland University of Technology Brisbane, Australia [email protected] @ snurb_dot_info http://mappingonlinepublics.net/

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Page 1: From Geographic Location to Network Location: The Potential of Big Social Data

From Geographic Location to Network Location: The Potential of Big Social DataProf. Axel BrunsARC Future FellowDigital Media Research CentreQueensland University of TechnologyBrisbane, [email protected] – @snurb_dot_info – http://mappingonlinepublics.net/

Page 2: From Geographic Location to Network Location: The Potential of Big Social Data

BIG DATA

Page 4: From Geographic Location to Network Location: The Potential of Big Social Data

BIG SOCIAL GEODATA?

Twitter Decahose English-language georeferenced tweets 23 October 2012 to 30 November 2012. (Leetaru et al., 2013 – http://firstmonday.org/article/view/4366/3654)

Page 5: From Geographic Location to Network Location: The Potential of Big Social Data

BIG SOCIAL GEODATA?

Network map showing locations of users retweeting other users (geocoded Twitter Decahose tweets 23 October 2012 to 30 November 2012) .(Leetaru et al., 2013 – http://firstmonday.org/article/view/4366/3654)

Page 6: From Geographic Location to Network Location: The Potential of Big Social Data

BIG SOCIAL GEODATA?

http://users.humboldt.edu/mstephens/hate/hate_map.html

Page 7: From Geographic Location to Network Location: The Potential of Big Social Data

GEOTAGGING IS UNCOMMON

Page 8: From Geographic Location to Network Location: The Potential of Big Social Data

GEOLOCATION? NETWORK LOCATION!

• Account information available from Twitter API:– Description (free text)– Location (free text)– Follower network– Twitter join date– Interface language– Interface timezone– Key stats (# followers, followees, tweets, etc.)

• Limitation:– Not available for ‘protected’ accounts (~3.5%)

Page 9: From Geographic Location to Network Location: The Potential of Big Social Data

MAPPING A NATIONAL TWITTERSPHERE

• Account information selected:– Description: mentions of Australian terms, top locations– Location: mentions of Australia, top locations

– Interface timezone: one of eight Australian state timezones

Page 10: From Geographic Location to Network Location: The Potential of Big Social Data

Education

Agriculture

Literature

Adelaide / SA

FoodWine

Beer

Parenting

Mums PR

Netizens

Marketing

InvestingReal Estate

Home BusinessSole Traders

Self-Help

HR / Support

Followback

Urban MediaUtilities

Advertising

Business

Fashion

Beauty

ArtsCinema

Journalists

Politics

Hard RightLeftists

News

CyclingTalkback

Music

TVV8s UFC

NRL

AFL

Football

Horse Racing

CricketNRU

Celebrities

Hillsong

Perth

PopMedia

Teen Idols

Cody Simpson

THE AUSTRALIAN TWITTERSPHERE

~140k Australian accounts with degree > 1000, as of Sep. 2013 (of a total 2.8m accounts found)

Page 11: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2006

Page 12: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2007

Page 13: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2008

Page 14: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2009

Page 15: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2010

Page 16: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2011

Page 17: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2012

Page 18: From Geographic Location to Network Location: The Potential of Big Social Data

ACCOUNTS CREATED IN 2013

Page 19: From Geographic Location to Network Location: The Potential of Big Social Data

AFL GRAND FINAL

Page 20: From Geographic Location to Network Location: The Potential of Big Social Data

Q&A

Page 21: From Geographic Location to Network Location: The Potential of Big Social Data

BACK TO GEOLOCATION?

Page 22: From Geographic Location to Network Location: The Potential of Big Social Data

NETWORK LOCATION GEOLOCATION?

• Inferring (typical) geolocation:– More local following than intercity/state/country following?

Can we infer your location from that of your followees?– More discussion of local than non-local issues?

Can we infer your location from your typical topics? Can we infer your location from your network’s topics?

• Combining network and geographic location data:– How does information travel across the network?– Does geographic location affect information flows here?

Page 23: From Geographic Location to Network Location: The Potential of Big Social Data

LIMITATIONS

• Twitter API policies:– Pursuit of short-term goals, not long-term strategies– Ill-conceived push to raise revenue through data sales– Counterproductive relationship with research community– Data access shaped to privilege certain limited methods

Most ‘big data’ Twitter research conducted by Twitter, Inc. and commercial research institutes

Page 24: From Geographic Location to Network Location: The Potential of Big Social Data

http://mappingonlinepublics.net/@snurb_dot_info@jeanburgess@tsadkowsky@petamitchell@flxvctr

@socialmediaQUT – http://socialmedia.qut.edu.au/ @qutdmrc – https://www.qut.edu.au/research/dmrc

This research is funded by the Australian Research Council through Future Fellowship and LIEF grants FT130100703 and LE140100148.