APART: Automatic Political Actor Recommendation in Real-time

  • Mohiuddin Solaimani
  • Sayeed Salam
  • Latifur Khan
  • Patrick T. Brandt
  • Vito D’OrazioEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)


Extracting actor data from news reports is important when generating event data. Hand-coded dictionaries are used to code actors and actions. Manually updating dictionaries for new actors and roles is costly and there is no automated method. We propose a dynamic frequency-based actor ranking algorithm with partial string matching for new actor-role detection, based on similar actors in the CAMEO dictionary. This is compared to a graph-based weighted label propagation baseline method. Results show our method outperforms the alternatives.


Edit Distance Parse Tree Recommended Actor Name Entity Recognition True Role 
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.



Support from the National Science Foundation (NSF) SBE-SMA-1539302, CNS-1229652, and SBE-SES-1528624; and the Air Force Office of Scientific Research (AFOSR): FA-9550-12-1-0077. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the NSF or the AFOSR.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    The Penn Treebank Project.
  5. 5.
  6. 6.
    Beieler, J., Brandt, P.T., Halterman, A., Schrodt, P.A., Simpson, E.M.: Generating political event data in near real time: opportunities and challenges. In: Michael Alvarez, R. (ed.) Computational Social Science: Discovery and Prediction, pp. 98–120. Cambridge University Press, Cambridge (2016)Google Scholar
  7. 7.
    Boschee, E., Lautenschlager, J., O’Brien, S., Shellman, S., Starz, J., Ward, M.: ICEWS Coded Event Data (2016)Google Scholar
  8. 8.
    Boschee, E., Natarajan, P., Weischedel, R.: Automatic extraction of events from open source text for predictive forecasting. In: Subrahmanian, V.S. (ed.) Handbook of Computational Approaches to Counterterrorism, pp. 51–67. Springer, New York (2013)CrossRefGoogle Scholar
  9. 9.
    Broder, A.Z.: On the resemblance and containment of documents. In: Compression and Complexity of Sequences 1997, Proceedings, pp. 21–29. IEEE (1997)Google Scholar
  10. 10.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707 (1966)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Lou, H., Li, S., Zhao, Y.: Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Phys. A Stat. Mech. Appl. 392(14), 3095–3105 (2013)CrossRefGoogle Scholar
  12. 12.
    O’Brien, S.: Crisis early warning and decision support: contemporary approaches and thoughts on future research. Int. Stud. Rev. 12(1), 87–104 (2010)CrossRefGoogle Scholar
  13. 13.
    Saraf, P., Ramakrishnan, N.: EMBERS autogsr: automated coding of civil unrest events. In: ACM SIGKDD, San Francisco, CA, USA, 13–17 August 2016, pp. 599–608 (2016)Google Scholar
  14. 14.
    Schrodt,P.A.: TABARI: Textual Analysis by Augmented Replacement Instructions (2009).
  15. 15.
    Schrodt, P.A., Davis, S.G., Weddle, J.L.: Political science: KEDS-a program for the machine coding of event data. Soc. Sci. Comput. Rev. 12(4), 561–587 (1994)CrossRefGoogle Scholar
  16. 16.
    Schrodt, P.A., Gerner, D.J., Yilmaz, Ö.: Conflict and mediation event observations (CAMEO): An event data framework for a post Cold War world. In: Bercovitch, J., Gartner, S. (eds.) International Conflict Mediation: New Approaches and Findings. Routledge, New York (2009)Google Scholar
  17. 17.
    Schrodt, P.A., Van Brackle, D.: Automated coding of political event data. In: Subrahmanian, V.S. (ed.) Handbook of Computational Approaches to Counterterrorism, pp. 23–49. Springer, New York (2013)CrossRefGoogle Scholar
  18. 18.
    Solaimani, M., Gopalan, R., Khan, L., Brandt, P.T., Thuraisingham, B.: Spark-based political event coding. In: BigDataService, pp. 14–23. IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of CSThe University of Texas at DallasRichardsonUSA
  2. 2.School of Economic, Political, and Policy SciencesThe University of Texas at DallasRichardsonUSA

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