TY - GEN
T1 - Quantifying political legitimacy from Twitter
AU - Liu, Haibin
AU - Lee, Dongwon
N1 - Funding Information:
Part of the work was done while Dongwon Lee visited the Air Force Research Lab (AFRL) at Rome, NY, in 2013, as a summer faculty fellow. Authors thank John Salerno at AFRL for the thoughful feedback on the idea and draft. This research was also in part supported by NSF awards of DUE-0817376, DUE-0937891, and SBIR-1214331.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-05579-4_14
DO - 10.1007/978-3-319-05579-4_14
M3 - Conference contribution
AN - SCOPUS:84958527842
SN - 9783319055787
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 111
EP - 118
BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings
PB - Springer Verlag
T2 - 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
Y2 - 1 April 2014 through 4 April 2014
ER -