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%.

中文翻译：

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用隐式半马尔可夫模型学习进化阶段，以预测社会动荡事件

社会动荡事件是现代社会中经常发生的事件，需要积极应对。一种有效的方法是不断评估即将发生的社会动荡事件的风险并预测这些事件的可能性。我们之前的工作建立了一个基于隐马尔可夫模型（HMM）的框架来预测与国家不稳定相关的指标，留下了两个可以优化的缺点：省略了事件参与者的互动，并隐式地学习了州的居住时间。受此启发，我们在本文中提出了一个新的预测框架，该框架使用了频繁的子图模式和隐藏的半马尔可夫模型（HSMM）。基于频繁子图挖掘和词袋模型，构造了称为BoEAG（事件包关联子图）的功能。新框架利用从GDELT（事件，位置和语调全球数据）中捕获的大规模数字历史事件来表征社会动乱事件演变阶段的过渡过程，揭示潜在的事件发展机制并制定社会动荡事件预测作为基于贝叶斯决策的序列分类问题。来自东南亚五个主要国家/地区的数据进行的实验结果证明了该新方法的有效性，该方法优于传统HMM 5.3％至16.8％，逻辑回归优于11.2％至43.6％。揭示潜在事件发展机制，并将社会动荡事件预测公式化为基于贝叶斯决策的序列分类问题。来自东南亚五个主要国家/地区的数据进行的实验结果证明了该新方法的有效性，该方法优于传统HMM 5.3％至16.8％，逻辑回归优于11.2％至43.6％。揭示潜在事件发展机制，并将社会动荡事件预测公式化为基于贝叶斯决策的序列分类问题。来自东南亚五个主要国家/地区的数据进行的实验结果证明了该新方法的有效性，该方法优于传统HMM 5.3％至16.8％，逻辑回归优于11.2％至43.6％。