Abstract
Social unrest events are common happenings in modern society which need to be proactively handled. An effective method is to continuously assess the risk of upcoming social unrest events and predict the likelihood of these events. Our previous work built a hidden Markov model- (HMM-) based framework to predict indicators associated with country instability, leaving two shortcomings which can be optimized: omitting event participants’ interaction and implicitly learning the state residence time. Inspired by this, we propose a new prediction framework in this paper, using frequent subgraph patterns and hidden semi-Markov models (HSMMs). The feature called BoEAG (Bag-of-Event-Association-subGraph) is constructed based on frequent subgraph mining and the bag of word model. The new framework leverages the large-scale digital history events captured from GDELT (Global Data on Events, Location, and Tone) to characterize the transitional process of the social unrest events’ evolutionary stages, uncovering the underlying event development mechanics and formulating the social unrest event prediction as a sequence classification problem based on Bayes decision. Experimental results with data from five main countries in Southeast Asia demonstrate the effectiveness of the new method, which outperforms the traditional HMM by 5.3% to 16.8% and the logistic regression by 11.2% to 43.6%.
Highlights
Opposition Democrat spokesman Chavanond Intarakomalyasut said the prime minister knows that the debate of the amnesty bill will lead to conflict but she was ready to take the risk in an attempt to whitewash criminal culprits including ousted prime minister aksin Shinawatra
In response to the above shortcomings, we propose a new prediction framework in this paper, using frequent subgraph patterns and hidden semi-Markov models (HSMMs). e new framework leverages the large-scale digital history events captured from GDELT to characterize the transitional process of the social unrest events’ evolutionary stages
(ii) Second, we propose the BoEAG (Bag-of-EventAssociation-subGraph) features to capture the characteristics of frequent patterns instead of the temporal burst patterns used in our previous work [8]. e original GDELT data within a certain time are represented as an event element association graph, from which the frequent subgraph patterns are mined
Summary
Since 2009, research studies on social unrest event prediction based on data mining have taken shape in some international political science journals [5, 10]. Parrish [39] used the recurrent neural network GRU sequence model and aggregated the GDELT event data by day, splicing them into feature vectors to determine whether a country has a social unrest event including domestic political crisis, riots, racial violence, and change of leadership. In [27], GDELT and ICEWS are used as data sources to predict unrest in Latin America In these works, comparatively little attention has been paid to consider the event evolutionary stages in the prediction models. We export the following GDELT event data for the experiments from the Google BigQuery web service
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