Automated analysis of the US presidential elections using Big Data and network analysis

Big Data and Society

Volumn 2, Issue 1, 2015, Pages

  Sudhahar, Saatviga ^a   Veltri, Giuseppe A ^b   Cristianini, Nello ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Big Data; computational social science; mediascape; network analysis; structural balance; subject verb object

Indexed keywords

EID: 84987634364     PISSN: None     EISSN: 20539517     Source Type: Journal    
DOI: 10.1177/2053951715572916     Document Type: Article

References (42)

1. Annis DH (2005) Permutation, parametric, and bootstrap tests of hypotheses. Journal of the American Statistical Association 100(472): 1457–1458.
2. Campbell A, Converse PE, Miller WE, (1966) Stokes 1960. The American Voter, New York, NY: Wiley.
3. Carrington PJ, Scott J, Wasserman S, (2005) Models and Methods in Social Network Analysis, Cambridge: Cambridge University Press.
4. Cartwright D, Harary F, (1956) Structural balance: A generalization of Heider's theory. Psychological Review 63(5): 277.
5. Conway III, LG Gornick LJ, Burfeind C, (2012) Does complex or simple rhetoric win elections? An integrative complexity analysis of us presidential campaigns. Political Psychology 33(5): 599–618.
6. Cristianini N, Shawe-Taylor J, (2000) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge: Cambridge University Press.
7. Cunningham H, (2002) Gate, a general architecture for text engineering. Computers and the Humanities 36(2): 223–254.
8. Davis JA, (1967) Clustering and structural balance in graph. Human Relations 20: 181–187.
9. De Fazio G (2013) Political radicalization in the making: The civil rights movement in Northern Ireland, 1968-1972. PhD Thesis, Emory University, Atlanta, GA.
10. Diesner J, Carley KM, Tambayong L, (2012) Extracting socio-cultural networks of the Sudan from open-source, large-scale text data. Computational & Mathematical Organization Theory. 1–12.
11. Doerfel ML, (1998) What constitutes semantic network analysis? A comparison of research and methodologies. Connections 21: 16–26.
12. Downs A, (1957) An Economic Theory of Democracy, New York, NY: Harper.
13. Earl J, Martin A, McCarthy JD, (2004) The use of newspaper data in the study of collective action. Annual Review of Sociology. 65–80.
14. Erdös P, Rényi A, (1960) On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5: 17–61.
15. Flaounas I, Ali O, Turchi M, et al. (2011) Noam: News outlets analysis and monitoring system. ACM SIGMOD International Conference on Management of Data, Athens, Greece.
16. Franzosi R, (1987) The press as a source of socio-historical data: Issues in the methodology of data collection from newspapers. Historical Methods: A Journal of Quantitative and Interdisciplinary History 20(1): 5–16.
17. Freeman LC, (1979) Centrality in social networks conceptual clarification. Social Networks 1(3): 215–239.
18. Gaskell G, Bauer M, Durant J, (1998) The representation of biotechnology: policy, media and public perception. Biotechnology in the Public Sphere. 3–8.
19. Good PI, (2005) Permutation, Parametric and Bootstrap Tests of Hypotheses, 3rd ed. New York: Springer.
20. Habermas J, Lennox S, Lennox F, (1974) The public sphere: An encyclopedia article (1964). New German Critique 3: 49–55.
21. Hamill R, Lodge M, Blake F, (1985) The breadth, depth, and utility of class, partisan, and ideological schemata. American Journal of Political Science. 850–870.
22. Hassan A, Abu-Jbara A, Radev D, (2012) Extracting signed social networks from text. Workshop of textgraphs-7 on graph-based methods for natural language processing, Jeju, Republic of Korea, 13 July. 6–14.
23. Hayes D, (2008) Party reputations, journalistic expectations: How issue ownership influences election news. Political Communication 25(4): 377–400.
24. Heider F, (1946) Attitudes and cognitive organization. The Journal of Psychology 21(1): 107–112.
25. Horn RA, Johnson CR, (2012) Matrix Analysis, Cambridge: Cambridge University Press.
26. Hu H, Wang X, (2009) Disassortative mixing in online social networks. EPL (Europhysics Letters) 86(1): 18003.
27. Kim L, (2011) Denotation and connotation in public representation: Semantic network analysis of Hwang supporters internet dialogues. Public Understanding of Science 22(3): 335–350.
28. Leskovec J, Huttenlocher D, Kleinberg J, (2010) Signed networks in social media. SIGCHI conference on human factors in computing systems, 10–15 April, 2010, Atlanta, Georgia, USA,. 1361–1370.
29. Lin D, (2003) Dependency-based evaluation of minipar. Treebanks 47: 317–329.
30. Mac Carron P, Kenna R, (2012) Universal properties of mythological networks. EPL (Europhysics Letters) 99(2): 28002.
31. McGee MC, (1980) The ideograph: A link between rhetoric and ideology. Quarterly Journal of Speech 66(1): 1–16.
32. Moretti F, (2013) Distant Reading, London: Verso Books.
33. Moscovici S, Duveen G, (2000) Social Representations: Explorations in Social Psychology, 41st ed. Cambridge: Polity Press.
34. Petrocik JR, (1996) Issue ownership and presidential elections. American Journal of Political Science 40: 825–850.
35. Petrocik JR, Benoit WL, Hansen GJ, (2003) Issue ownership and presidential campaigning, 1952–2000. Political Science Quarterly 118: 599–626.
36. Shannon P, Andrew M, Owen O, (2003) Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research 13(11): 2498–2504.
37. Sudhahar S, Fazio GD, Franzosi R, et al. (2013) Network analysis of narrative content in large corpora. Natural Language Engineering. Available on CJO2013. DOI:10.1017/S1351324913000247.
38. Wasserman S, (1994) Social Network Analysis: Methods and Applications, 8th ed. Cambridge: Cambridge University press.
39. Welch WJ, (1990) Construction of permutation tests. Journal of the American Statistical Association 85(411): 693–698.
40. Wilson EB, (1927) Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association 22(158): 209–212.
41. Wilson T, Hoffmann P, Somasundaran S, (2005) Opinionfinder: A system for subjectivity analysis. HLT/EMNLP on interactive demonstrations, Vancouver, 6--8 October. 34–35.
42. Yuan EJ, Feng M, Danowski JA, (2013) Privacy in semantic networks on Chinese social media: The case of Sina Weibo. Journal of Communication. DOI: 10.1111/jcom.12058.