We present a method to quantify the political legitimacy of a populace using public Twitter data. First, we represent the notion of legitimacy with respect to k-dimensional probabilistic topics, automatically culled from the politically oriented corpus. The short tweets are then converted to a feature vector in k-dimensional topic space. Leveraging sentiment analysis, we also consider the polarity of each tweet. Finally, we aggregate a large number of tweets into a final legitimacy score (i.e., L-score) for a populace. To validate our proposal, we conduct an empirical analysis on eight sample countries using related public tweets, and find that some of our proposed methods yield L-scores strongly correlated with those reported by political scientists.


Latent Dirichlet Allocation Sentiment Analysis Political Legitimacy Social Media Data Polarity Score 
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  1. 1.
    Gilley, B.: The meaning and measure of state legitimacy: Results for 72 countries. European J. of Political Research 45(3), 499–525 (2006)CrossRefGoogle Scholar
  2. 2.
    Gilley, B.: State legitimacy: An updated dataset for 52 countries. European J. of Political Research 51(5), 693–699 (2012)CrossRefGoogle Scholar
  3. 3.
    Salerno, J.J., Romano, B., Geiler, W.: The national operational environment model (noem). In: SPIE Modeling and Simulation for Defense Systems & App. (2011)Google Scholar
  4. 4.
    Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: ACM SIGIR, pp. 841–842 (2010)Google Scholar
  5. 5.
    Paul, M.J., Dredze, M.: You are what you tweet: Analyzing twitter for public health. In: ICWSM (2011)Google Scholar
  6. 6.
    Lamb, A., Paul, M.J., Dredze, M.: Investigating twitter as a source for studying behavioral responses to epidemics. In: AAAI Fall Symp. (2012)Google Scholar
  7. 7.
    Evans, J., Fast, S., Markuzon, N.: Modeling the social response to a disease outbreak. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 154–163. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. of Computational Science 2(1), 1–8 (2011)CrossRefGoogle Scholar
  9. 9.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. In: ICWSM, pp. 178–185 (2010)Google Scholar
  10. 10.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Election forecasts with twitter how 140 characters reflect the political landscape. Social Science Computer Review 29(4), 402–418 (2011)CrossRefGoogle Scholar
  11. 11.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. In: ICWSM (2010)Google Scholar
  12. 12.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  13. 13.
    Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: ICWSM (2011)Google Scholar
  14. 14.
    Rodgers, J., Nicewander, A.: Thirteen ways to look at the correlation coefficient. The American Statistician 42(1), 59–66 (1988)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haibin Liu
    • 1
  • Dongwon Lee
    • 1
  1. 1.College of Information Sciences and TechnologyThe Pennsylvania State UniversityUniversity ParkUSA

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