Predicting Social Unrest Events with Hidden Markov Models Using GDELT

Proactive handling of social unrest events which are common happenings in both democracies and authoritarian regimes requires that the risk of upcoming social unrest event is continuously assessed. Most existing approaches comparatively pay little attention to considering the event development stages. In this paper, we use autocoded events dataset GDELT (Global Data on Events, Location, and Tone) to build a Hidden Markov Models (HMMs) based framework to predict indicators associated with country instability. The framework utilizes the temporal burst patterns in GDELT event streams to uncover the underlying event development mechanics and formulates the social unrest event prediction as a sequence classification problem based on Bayes decision. Extensive experiments with data from five countries in Southeast Asia demonstrate the effectiveness of this framework, which outperforms the logistic regression method by 7% to 27% and the baseline method 34% to 62% for various countries.

[1]  C. McClelland.  World event/interaction survey , 1978 .

[2]  E. Azar.  The Conflict and Peace Data Bank (COPDAB) Project , 1980 .

[3]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[4]  J. Jenkins,et al.  Mapping Mass Political Conflict and Civil Society , 1997 .

[5]  Susan T. Dumais,et al.  Newsjunkie: providing personalized newsfeeds via analysis of information novelty , 2004, WWW '04.

[6]  Felix Salfner.  Predicting Failures with Hidden Markov Models , 2005 .

[7]  M. Osborne,et al.  Using Prediction Markets and Twitter to Predict a Swine Flu Pandemic , 2009 .

[8]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.

[9]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[10]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[11]  Sean P. O'Brien,et al.  Crisis Early Warning and Decision Support: Contemporary Approaches and Thoughts on Future Research , 2010 .

[12]  Heeyoung Lee,et al.  Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task , 2011, CoNLL Shared Task.

[13]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[14]  Benyuan Liu,et al.  Predicting Flu Trends using Twitter data , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[15]  Xiaofeng Wang,et al.  Automatic Crime Prediction Using Events Extracted from Twitter Posts , 2012, SBP.

[16]  Dylan D. Schmorrow,et al.  Lessons learned in instability modeling, forecasting and mitigation from the DARPA Integrated Crisis Early Warning System (ICEWS) Program , 2012 .

[17]  Argimiro Arratia,et al.  Forecasting with twitter data , 2013, ACM Trans. Intell. Syst. Technol..

[18]  Doug Bond,et al.  Using Hidden Markov Models to Predict Terror Before it Hits (Again) , 2013 .

[19]  Eric Horvitz,et al.  Mining the web to predict future events , 2013, WSDM.

[20]  Ehsan Alikhani.  Computational Social Analysis: Social Unrest Prediction Using Textual Analysis of News , 2014 .

[21]  Aravind Srinivasan,et al.  'Beating the news' with EMBERS: forecasting civil unrest using open source indicators , 2014, KDD.

[22]  Sathappan Muthiah,et al.  Forecasting Protests by Detecting Future Time Mentions in News and Social Media , 2014 .

[23]  Naren Ramakrishnan,et al.  Detecting and forecasting domestic political crises: a graph-based approach , 2014, WebSci '14.

[24]  Nathan Kallus,et al.  Predicting crowd behavior with big public data , 2014, WWW.

[25]  Jiejun Xu,et al.  Using publicly visible social media to build detailed forecasts of civil unrest , 2014, Security Informatics.

[26]  Naren Ramakrishnan,et al.  Combining heterogeneous data sources for civil unrest forecasting , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[27]  Liang Zhao,et al.  Spatiotemporal Event Forecasting in Social Media , 2015, SDM.

[28]  Hui Wang,et al.  Computational Approach to Detecting and Predicting Occupy Protest Events , 2015, 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI).

[29]  Wael Khreich,et al.  A Survey of Techniques for Event Detection in Twitter , 2015, Comput. Intell..

[30]  Jose Cadena,et al.  Forecasting Social Unrest Using Activity Cascades , 2015, PloS one.

[31]  Naren Ramakrishnan,et al.  Planned Protest Modeling in News and Social Media , 2015, AAAI.