This article outlines a new method for investigating social position through geo-tagged Twitter data, specifically through the application of the geodemographic classification system Mosaic. The method involves the identification of a given tweeter’s likely location of residence from the ‘geo-tag’ attached to their tweet. Using this high-resolution geographic information, each individual tweet is then attributed a geodemographic classification. This article shows that the specific application of geodemographics for discerning between different types of tweeters is problematic in some ways, but that the general process of classifying tweeters according to their position in geographical space is viable and represents a powerful new method for discerning the social position of tweeters. Further research is required in this area, as there is great potential in employing the mobile global positioning system data appended to digital by-product data to explore the intersections between geographical space and social position.
BeerDBurrowsR (2007) Sociology and, of and in Web 2.0: Some initial considerations. Sociological Research Online12(5): 17.
2.
BeerDTaylorM (2013) The hidden dimensions of the musical field and the potential of the new social data. Sociological Research Online18(2): 14.
3.
BourdieuP (2001) The forms of capital. In: GranovetterMSwedbergR (eds) The Sociology of Economic Life. Boulder, CO: Westview Press, pp. 96–111.
4.
BurgerJHendersonJKimGet al. (2011) Discriminating gender on Twitter. In: Proceedings of the conference on empirical methods in natural language processing, Edinburgh, 27–31 July, pp. 1301–1309. Stroudsburg, PA: Association for Computational Linguistics.
5.
BurnapPRanaOMAvisNet al (2015) Detecting tension in online communities with computational Twitter analysis. Technological Forecasting and Social Change95: 96–108.
6.
BurrowsRGaneN (2006) Geodemographics, software and class. Sociology40(5): 793–812.
7.
BurrowsRSavageM (2014) After the crisis? Big Data and the methodological challenges of empirical sociology. Big Data & Society1(1): 1–6.
8.
BurrowsRWebberRAtkinsonR (2016) Welcome to ‘Pikettyville’? Mapping London’s Alpha Territories. The Sociological Review65(2): 184–201.
9.
CrangMGrahamS (2007) Sentient cities ambient intelligence and the politics of urban space. Information, Communication & Society10(6): 789–817.
10.
CromptonR (2008) Class and Stratification. London: Polity Press.
11.
DunbarRArnaboldiVContiMet al. (2015) The structure of online social networks mirrors those in the offline world. Social Networks43: 39–47.
12.
Experian (2014) Endless Possibilities. One Mosaic. London: Experian.
13.
FeltM (2016) Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society. Epub ahead of print 29April. DOI: 10.1177/2053951716645828.
14.
GleasonB (2013) #Occupy wall street: Exploring informal learning about a social movement on Twitter. American Behavioral Scientist57(7): 966–982.
15.
GrahamMHaleSGaffneyD (2014) Where in the world are you? Geolocation and language identification in Twitter. The Professional Geographer66(4): 568–578.
16.
GrahamS (2005) Software-sorted geographies. Progress in Human Geography29(5): 562–580.
17.
HarrisRSleightPWebberR (2005) Geodemographics, GIS and Neighbourhood Targeting, vol. 7. Hoboken, NJ: John Wiley & Sons.
18.
JansenBZhangMSobelKet al (2009) Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology60(11): 2169–2188.
19.
LotanGGraeffEAnannyMet al (2011) The Arab Spring| the revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions. International Journal of Communication5: 31.
ParkerSUprichardEBurrowsR (2007) Class places and place classes geodemographics and the spatialization of class. Information, Communication & Society10(6): 902–921.
22.
PrandyKLambertP (2003) Marriage, social distance and the social space: An alternative derivation and validation of the Cambridge Scale. Sociology37(3): 397–411.
23.
RogersR (2009) The End of the Virtual: Digital Methods. Amsterdam: Amsterdam University Press.
24.
SavageMBurrowsR (2007) The coming crisis of empirical sociology. Sociology41(5): 885–899.
25.
SavageMBurrowsR (2009) Some further reflections on the coming crisis of empirical sociology. Sociology43(4): 762–772.
26.
SavageMBagnallGLonghurstB (2005) Globalization and Belonging. Thousand Oaks, CA: SAGE.
27.
SloanLMorganJ (2015) Who tweets with their location? Understanding the relationship between demographic characteristics and the use of geoservices and geotagging on Twitter. PLoS ONE10(11): e0142209.
28.
SloanLMorganJBurnapPet al (2015) Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS ONE10(3): e0115545.
29.
SloanLMorganJHousleyWet al (2013) Knowing the tweeters: Deriving sociologically relevant demographics from Twitter. Sociological Research Online18(3): 7.
StewartAPrandyKBlackburnR (1973) Measuring the class structure. Nature245: 415–417.
33.
TseYKet al. (2017) Exploring the hidden pattern from tweets: Investigation into Volkswagen emissions scandal. Supply Chain Management in the Big data Era. IGI Global: 172–98.
34.
TukeyJW (1977) Exploratory Data Analysis. Reading, MA: Addison-Wesley.
35.
UprichardEBurrowsRByrneD (2008) SPSS as an ‘inscription device’: From causality to description?The Sociological Review56(4): 606–622.
36.
UprichardEBurrowsRParkerS (2009) Geodemographic code and the production of space. Environment and Planning A41(12): 2823–2835.
37.
WebberR (2004) The relative power of geodemographics vis a vis person and household level demographic variables as discriminators of consumer behaviour. CASA working paper series no 84. London: CASA. Available at http://www.wholesem.ac.uk/bartlett/casa/pdf/paper84.pdf (accessed 7th August 2017).
38.
WebberR (2009) Response to the coming crisis of empirical sociology’: An outline of the research potential of administrative and transactional data. Sociology43(1): 169–178.
39.
WebberRButlerT (2007) Classifying pupils by where they live: How well does this predict variations in their GCSE results?Urban Studies44(7): 1229–1253.
