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Abstract

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.

Keywords

Latent Dirichlet Allocation Sentiment Analysis Political Legitimacy Social Media Data Polarity Score 
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.

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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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