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Automatic Mapping of Social Networks of Actors from Text Corpora: Time Series Analysis

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Data Mining for Social Network Data

Part of the book series: Annals of Information Systems ((AOIS,volume 12))

Abstract

To test hypotheses about presidential cabinet network centrality and presidential job approval over time and to illustrate automatic social network identification from large volumes of text, this research mined the social networks among the cabinets of Presidents Reagan, G.H.W. Bush, Clinton, and G.W. Bush based on the members’ co-occurrence in news stories. Each administration’s data was sliced into time intervals corresponding to the Gallup presidential approval polls to synchronize the social networks with presidential job approval ratings. It was hypothesized that when the centrality of the president is lower than that of other cabinet members, job approval ratings are higher. This is based on the assumption that news is generally negative and when the president stands above the other cabinet members in network centrality, he or she is more likely to be associated with the negative press coverage in the minds of members of the public. The hypothesis was supported for each of the administrations with the Reagan and G.H.W. Bush having a lag of 1, Clinton a lag of 4, and G.W. Bush a lag of 2. Automatic network analysis of social actors from textual corpora is feasible and enables testing hypotheses over time.

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References

  1. Adamic, L., and Glace, N. The political blogosphere and the 2004 U.S. election: Divided they blog. In LinkKDD ’05: Proceedings of the 3rd International Workshop on Link Discovery, Chicago, IL, pp. 36–43, 2005.

    Google Scholar 

  2. Batagelj, V. Pajek: Program for large network analysis. Connections, 21(2):47, 1998.

    Google Scholar 

  3. Baudrillard, J. Simulacra. Translated by S.F. Glaser. Ann Arbor, MI: University of Michigan Press, 1994.

    Google Scholar 

  4. Bonacich, P., Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2:113–120, 1972.

    Article  Google Scholar 

  5. Borgatti, S.P. NetDraw: Graph visualization software. Harvard, MA: Analytic Technologies, 2002.

    Google Scholar 

  6. Borgatti, S.P. Centrality and network flow. Social Networks, 27:55–71, 2005.

    Article  Google Scholar 

  7. Borgatti, S.P., Everett, M.G., and Freeman, L.C. UCINET for windows: Software for social network analysis. Harvard, MA: Analytic Technologies, 2002.

    Google Scholar 

  8. Cepela, N., and Danowski, J.A. Automatic mapping of political networks of actors from large collections of news stories. In Proceedings of the 1st ASONAM Conference, Athens, Greece, 2009.

    Google Scholar 

  9. Cohen, J.E. The polls: The components of presidential favorability. Presidential Studies Quarterly, 30(1):169–177, 2000.

    Article  Google Scholar 

  10. Dahl, R. A critique of the ruling-elite model. American Political Science Review, 52:463–469, 1961.

    Google Scholar 

  11. Danowski, J.A. A network-based content analysis methodology for computer-mediated communication: An illustration with a computer bulletin board. In M. Burgoon (ed.), Communication Yearbook 5, pp. 904–925. New Brunswick, NJ: Transaction Books, 1982.

    Google Scholar 

  12. Danowski, J.A. Organizational infographics and automated auditing: Using computers to unobtrusively gather and analyze communication. In G. Goldhaber and G. Barnett (eds.), Handbook of Organizational Communication, pp. 335–384, Norwood, NJ: Ablex, 1988.

    Google Scholar 

  13. Danowski, J.A. Network analysis of message content. In G. Barnett and W. Richards (eds), Progress in Communication Sciences XII, pp. 197–222, Norwood, NJ: Ablex, 1993a.

    Google Scholar 

  14. Danowski, J.A. WORDij: A word pair approach to information retrieval. In Proceedings of the DARPA/NIST TREC Conference, Washington, DC: National Institute of Standards and Technology, pp. 131–136, 1993b.

    Google Scholar 

  15. Danowski, J.A. WORDij 3.0 [computer program]. Chicago, IL: University of Illinois at Chicago, 2009a. http://WORDij.net

  16. Danowski, J.A. Inferences from word networks in messages. In Krippendorff, K. and Bock, M.A (eds), The Content Analysis Reader, pp. 421–429, Thousand Oaks, CA: Sage Publications, 2009b.

    Google Scholar 

  17. Dezsö, Z., Almaas, E., Lukács, A., Rácz, B., Szakadát, I., and Barabási, A.L. Dynamics of information access on the web. Physical Review E, 73, 066132-1-066132-6, 2006.

    Google Scholar 

  18. Diesner, J. and Carley, K. AutoMap 1.2: Extract, Analyze, Represent, and Compare Mental Models from Texts, 2004. reports-archive.adm.cs.cmu.edu

    Google Scholar 

  19. Entman, R.M. Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4):51–58, 1993.

    Article  Google Scholar 

  20. Entman, R.M. Framing bias: Media in the distribution of power. Journal of Communication, 57:163–173, 2007.

    Article  Google Scholar 

  21. Farrell, H. and Drezner, D.W. The power and politics of blogs. Public Choice, 134:15–30, 2008.

    Article  Google Scholar 

  22. Fenno, R.F. The President’s Cabinet: An Analysis in the Period from Wilson to Eisenhower. Cambridge, MA: Harvard University Press, 1959.

    Google Scholar 

  23. Freeman, L.C. A set of measures of centrality based on betweenness. Sociometry, 40(1): 35–41, 1977.

    Article  Google Scholar 

  24. Galaskiewicz, J. The structure of community organizational networks. Social Forces, 57(4):1346–1364, 1979.

    Google Scholar 

  25. Gronke, P. and Newman, B. FDR to Clinton, Mueller to ?: A field essay on presidential approval. Political Research Quarterly, 56(4):501–512, 2000.

    Google Scholar 

  26. Hanneman, R.A. and Riddle, M. Introduction to Social Network Methods. Riverside, CA: University of California, 2005, Riverside (http://faculty.ucr.edu/~hanneman/)

  27. Hunter, F. Community Power Structure. Chapel Hill: University of North Carolina Press, 1953.

    Google Scholar 

  28. Jones, S. Television news: Geographic and source biases, 1982–2004. International Journal of Communication. 2:223–250, 2008.

    Google Scholar 

  29. Katz, E., and Lazarsfeld, P. Personal Influence. New York, NY: Free Press, 1955.

    Google Scholar 

  30. Knoke, D. Political Networks: The Structural Perspective. Cambridge :Cambridge University Press, 1994.

    Google Scholar 

  31. Lazarsfeld, B., Berelson, B., and Gaudet, H. The People’s Choice. New York, NY: Columbia University Press, 1948.

    Google Scholar 

  32. McCombs, M.E., and Shaw, D.L. The agenda-setting function of mass media, The Public Opinion Quarterly, 36(2):176–187, 1972.

    Article  Google Scholar 

  33. Nicholson, S.P., Segura, G.M., and Woods, N.D. Presidential approval and the mixed blessing of divided government. The Journal of Politics, 64(3):701–720, 2002.

    Article  Google Scholar 

  34. Polsby, N. Community Power and Political Theory. New Haven, CT: Yale University Press, 1963.

    Google Scholar 

  35. Rousseau, R., and Zhang, L. Betweenness centrality and Q-measures in directed valued networks. Scientometrics, 75(3):575–590, 2008.

    Article  Google Scholar 

  36. Swales, Jr., G.S., and Yoon, Y. Applying artificial neural networks to investment analysis. Financial Analysts Journal, 48(5):78–80, 1992.

    Article  Google Scholar 

  37. Tichy, N.M., Tushman, M., and Fombrun, C. Social network analysis for organizations. The Academy of Management Review, 4(4):507–519, 1979.

    Google Scholar 

  38. Zhai, Y., Hsu, A., and Halgamuge, K. Combining news and technical indicators in daily stock price trends prediction, In Liu et al. (eds), ISNN 2007, Part III, LNCS 4493, pp. 1087–1096. Berlin: Springer-Verlag Berlin Heidelberg.

    Google Scholar 

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Acknowledgments

The authors are grateful for Rafal Radulski and his timely, efficient, and effective programming assistance and for his excellent communication skills. The authors are thankful for the insightful comments of two anonymous reviewers. This research is supported by National Science Foundation Grant 0527487, Human and Social Dynamics Program.

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Correspondence to James A. Danowski .

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Danowski, J.A., Cepela, N. (2010). Automatic Mapping of Social Networks of Actors from Text Corpora: Time Series Analysis. In: Memon, N., Xu, J., Hicks, D., Chen, H. (eds) Data Mining for Social Network Data. Annals of Information Systems, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6287-4_3

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