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Timely Decision Analysis Enabled by Efficient Social Media Modeling

Author

Listed:
  • Theodore T. Allen

    (Integrated Systems Engineering, Ohio State University, Columbus, Ohio 43210)

  • Zhenhuan Sui

    (Integrated Systems Engineering, Ohio State University, Columbus, Ohio 43210)

  • Nathan L. Parker

    (TRADOC Analysis Center, Monterey Naval Postgraduate School, Monterey, California 93943)

Abstract

Many decision problems are set in changing environments. For example, determining the optimal investment in cyber maintenance depends on whether there is evidence of an unusual vulnerability, such as “Heartbleed,” that is causing an especially high rate of incidents. This gives rise to the need for timely information to update decision models so that optimal policies can be generated for each decision period. Social media provide a streaming source of relevant information, but that information needs to be efficiently transformed into numbers to enable the needed updates. This article explores the use of social media as an observation source for timely decision making. To efficiently generate the observations for Bayesian updates, we propose a novel computational method to fit an existing clustering model. The proposed method is called k -means latent Dirichlet allocation (KLDA). We illustrate the method using a cybersecurity problem. Many organizations ignore “medium” vulnerabilities identified during periodic scans. Decision makers must choose whether staff should be required to address these vulnerabilities during periods of elevated risk. Also, we study four text corpora with 100 replications and show that KLDA is associated with significantly reduced computational times and more consistent model accuracy.

Suggested Citation

  • Theodore T. Allen & Zhenhuan Sui & Nathan L. Parker, 2017. "Timely Decision Analysis Enabled by Efficient Social Media Modeling," Decision Analysis, INFORMS, vol. 14(4), pages 250-260, December.
  • Handle: RePEc:inm:ordeca:v:14:y:2017:i:4:p:250-260
    DOI: 10.1287/deca.2017.0360
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    References listed on IDEAS

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    1. Homem-de-Mello, Tito & Pagnoncelli, Bernardo K., 2016. "Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective," European Journal of Operational Research, Elsevier, vol. 249(1), pages 188-199.
    2. Xing Gao & Weijun Zhong & Shue Mei, 2013. "Information Security Investment When Hackers Disseminate Knowledge," Decision Analysis, INFORMS, vol. 10(4), pages 352-368, December.
    3. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
    4. Gregory S. Parnell & Rudolph E. Butler & Stephen J. Wichmann & Mike Tedeschi & David Merritt, 2015. "Air Force Cyberspace Investment Analysis," Decision Analysis, INFORMS, vol. 12(2), pages 81-95, June.
    5. Yongzhi Cao, 2014. "Reducing Interval-Valued Decision Trees to Conventional Ones: Comments on Decision Trees with Single and Multiple Interval-Valued Objectives," Decision Analysis, INFORMS, vol. 11(3), pages 204-212, September.
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    Cited by:

    1. Chen, Jiawen & Liu, Linlin, 2023. "Social media usage and entrepreneurial investment: An information-based view," Journal of Business Research, Elsevier, vol. 155(PB).
    2. Ali E. Abbas & Jay Simon & Chris Smith, 2017. "Introduction to the Special Issue on Decision Analysis and Social Media," Decision Analysis, INFORMS, vol. 14(4), pages 227-228, December.
    3. Vicki M. Bier & Simon French, 2020. "From the Editors: Decision Analysis Focus and Trends," Decision Analysis, INFORMS, vol. 17(1), pages 1-8, March.

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