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

  • James A. DanowskiEmail author
  • Noah Cepela
Part of the Annals of Information Systems book series (AOIS, volume 12)


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.


Social Network Analysis Time Slice Network Centrality News Story Bush Administration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


  1. 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. 2.
    Batagelj, V. Pajek: Program for large network analysis. Connections, 21(2):47, 1998.Google Scholar
  3. 3.
    Baudrillard, J. Simulacra. Translated by S.F. Glaser. Ann Arbor, MI: University of Michigan Press, 1994.Google Scholar
  4. 4.
    Bonacich, P., Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2:113–120, 1972.CrossRefGoogle Scholar
  5. 5.
    Borgatti, S.P. NetDraw: Graph visualization software. Harvard, MA: Analytic Technologies, 2002.Google Scholar
  6. 6.
    Borgatti, S.P. Centrality and network flow. Social Networks, 27:55–71, 2005.CrossRefGoogle Scholar
  7. 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. 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. 9.
    Cohen, J.E. The polls: The components of presidential favorability. Presidential Studies Quarterly, 30(1):169–177, 2000.CrossRefGoogle Scholar
  10. 10.
    Dahl, R. A critique of the ruling-elite model. American Political Science Review, 52:463–469, 1961.Google Scholar
  11. 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. 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. 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. 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. 15.
    Danowski, J.A. WORDij 3.0 [computer program]. Chicago, IL: University of Illinois at Chicago, 2009a.
  16. 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. 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. 18.
    Diesner, J. and Carley, K. AutoMap 1.2: Extract, Analyze, Represent, and Compare Mental Models from Texts, 2004. reports-archive.adm.cs.cmu.eduGoogle Scholar
  19. 19.
    Entman, R.M. Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4):51–58, 1993.CrossRefGoogle Scholar
  20. 20.
    Entman, R.M. Framing bias: Media in the distribution of power. Journal of Communication, 57:163–173, 2007.CrossRefGoogle Scholar
  21. 21.
    Farrell, H. and Drezner, D.W. The power and politics of blogs. Public Choice, 134:15–30, 2008.CrossRefGoogle Scholar
  22. 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. 23.
    Freeman, L.C. A set of measures of centrality based on betweenness. Sociometry, 40(1): 35–41, 1977.CrossRefGoogle Scholar
  24. 24.
    Galaskiewicz, J. The structure of community organizational networks. Social Forces, 57(4):1346–1364, 1979.Google Scholar
  25. 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. 26.
    Hanneman, R.A. and Riddle, M. Introduction to Social Network Methods. Riverside, CA: University of California, 2005, Riverside (
  27. 27.
    Hunter, F. Community Power Structure. Chapel Hill: University of North Carolina Press, 1953.Google Scholar
  28. 28.
    Jones, S. Television news: Geographic and source biases, 1982–2004. International Journal of Communication. 2:223–250, 2008.Google Scholar
  29. 29.
    Katz, E., and Lazarsfeld, P. Personal Influence. New York, NY: Free Press, 1955.Google Scholar
  30. 30.
    Knoke, D. Political Networks: The Structural Perspective. Cambridge :Cambridge University Press, 1994.Google Scholar
  31. 31.
    Lazarsfeld, B., Berelson, B., and Gaudet, H. The People’s Choice. New York, NY: Columbia University Press, 1948.Google Scholar
  32. 32.
    McCombs, M.E., and Shaw, D.L. The agenda-setting function of mass media, The Public Opinion Quarterly, 36(2):176–187, 1972.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 34.
    Polsby, N. Community Power and Political Theory. New Haven, CT: Yale University Press, 1963.Google Scholar
  35. 35.
    Rousseau, R., and Zhang, L. Betweenness centrality and Q-measures in directed valued networks. Scientometrics, 75(3):575–590, 2008.CrossRefGoogle Scholar
  36. 36.
    Swales, Jr., G.S., and Yoon, Y. Applying artificial neural networks to investment analysis. Financial Analysts Journal, 48(5):78–80, 1992.CrossRefGoogle Scholar
  37. 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. 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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© Springer US 2010

Authors and Affiliations

  1. 1.Department of CommunicationUniversity of IllinoisChicagoUSA

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