In this paper we deal with Twitter and the presence of the keyword “Macedonia”
in tweets over a period of time. We searched for the same term in three
different languages, i.e. “Μακεδονíα”, “Macedonia” and “Македонска -
Македониjа”, since we are primarily interested in views from Greece and
FYROM without excluding views from other regions. We use methods from
Social Network Analysis (SNA) in order to create networks of users, calculate
some main network metrics, measure user importance and investigate the
presence of possible fragmentations—communities among them. We furthermore
proceed to a form of content analysis, using pairs of words within
tweets, in order to obtain main ideas, trends and public views that circulated
over the network.
References
[1]
Filippas, N. (2015) Market Psychology, Intervantions from the Recent Financial Crisis. Pedio, Athens. (In Greek)
[2]
Oxford Internet Institute (2016) Five Pieces You Should Probably Read On: Fake News and Filter Bubbles.
https://www.oii.ox.ac.uk/five-pieces-you-should-probably-read-on-fake-news-and-filter-bubbles/
[3]
Kydros, D. (2018) Twitting Bad Rumours—The Grexit Case. International Journal of Web Based Communities, 14, 4-20. https://doi.org/10.1504/IJWBC.2018.090933
[4]
Rogstad, I. (2016) Is Twitter just Rehashing? Intermedia Agenda Setting between Twitter and Mainstream Media. Journal of Information Technology & Politics, 13, 1-17. https://doi.org/10.1080/19331681.2016.1160263
[5]
Freeman, L.C. (2004) The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press, Vancouver.
[6]
Wasserman, S. and Faust, K. (1994) Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge.
https://doi.org/10.1017/CBO9780511815478
[7]
Knoke, D. and Kuklinski, J. (1982) Network Analysis. Sage Publications, Beverly Hills.
[8]
Marin, A. and Wellman, B. (2011) Social Network Analysis: An Introduction. In: Scott, J. and Carrington, P.J., Eds., The Sage Handbook of Social Network Analysis, Sage Publications, Thousand Oaks, 11-25.
[9]
Wellman, B. (1983) Network Analysis: Some Basic Principles. Sociological Theory, 1, 155-200. https://doi.org/10.2307/202050
[10]
Smith, M., Ceni A., Milic-Frayling, N., Shneiderman, B., Mendes Rodrigues, E., Leskovec, J. and Dunne, C. (2010) NodeXL: A Free and Open Network Overview, Discovery and Exploration Add-In for Excel 2007/2010/2013/2016. Social Media Research Foundation. https://www.smrfoundation.org
[11]
Clauset, A., Newman, M.E.J. and Moore, C. (2005) Finding Community Structure in Very Large Networks. Physical Review, 70, 66-111.
[12]
Watts, D.J. and Strogatz, S.H. (1998) Collective Dynamics of “Small-World” Networks. Nature, 393, 440-442. https://doi.org/10.1038/30918
[13]
Gorodnichenko, Y., Pham, T. and Talavera, O. (2018) Social Media, Sentiment and Public Opinions: Evidence from #BREXIT and #USAELECTION. Working Paper, National Bureau of Economic Research. http://www.nber.org/papers/w24631.pdf
[14]
Danowski, J.A. (2010) Inferences from Word Networks in Messages. In: Krippendorff, K. and Bock, M., Eds., The Content Analysis Reader, Sage Publications, Thousand Oaks, 421-430.