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.

Download Full-text

Application of the hidden Markov models, kNN and SVM for a classification problem modes of power supply system

2012 IEEE 11th International Conference on Actual Problems of Electronics Instrument Engineering (APEIE) ◽

10.1109/apeie.2012.6629050 ◽

2012 ◽

Cited By ~ 1

Author(s):

T. A. Gultyaeva ◽

D. Y. Korotenko ◽

A. A. Popov

Keyword(s):

Hidden Markov Models ◽

Power Supply ◽

Power Supply System ◽

Markov Models ◽

Hidden Markov ◽

Classification Problem ◽

Supply System

Download Full-text

Object-Based Scene Classification Modeled by Hidden Markov Models Architecture

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.20211001oa42 ◽

2021 ◽

Vol 15 (4) ◽

pp. 0-0

Keyword(s):

Markov Chains ◽

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Classification Problem ◽

Mathematical Tool ◽

Scene Classification ◽

Classification Problems ◽

Object Based ◽

Inference Algorithms

Multiclass classification problems such as document classification, medical diagnosis or scene classification are very challenging to address due to similarities between mutual classes. The use of reliable tools is necessary to get good classification results. This paper addresses the scene classification problem using objects as attributes. The process of classification is modeled by a famous mathematical tool: The Hidden Markov Models. We introduce suitable relations that scale the parameters of the Hidden Markov Model into variables of scene classification. The construction of Hidden Markov Chains is supported with weight measures and sorting functions. Lastly, inference algorithms extract most suitable scene categories from the Discrete Markov Chain. A parallelism approach constructs several Discrete Markov Chains in order to improve the accuracy of the classification process. We provide numerous tests on different datasets and compare classification accuracies with some state of the art methods. The proposed approach distinguishes itself by outperforming the other.

Download Full-text

Maximum margin hidden Markov models for sequence classification

Pattern Recognition Letters ◽

10.1016/j.patrec.2016.03.017 ◽

2016 ◽

Vol 77 ◽

pp. 14-20 ◽

Cited By ~ 3

Author(s):

Nikolaus Mutsam ◽

Franz Pernkopf

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Sequence Classification ◽

Maximum Margin

Download Full-text

Hidden Markov models for gene sequence classification

Pattern Analysis and Applications ◽

10.1007/s10044-015-0508-9 ◽

2015 ◽

Vol 19 (3) ◽

pp. 793-805 ◽

Cited By ~ 2

Author(s):

Andrea Mesa ◽

Sebastián Basterrech ◽

Gustavo Guerberoff ◽

Fernando Alvarez-Valin

Keyword(s):

Hidden Markov Models ◽

Gene Sequence ◽

Markov Models ◽

Hidden Markov ◽

Sequence Classification

Download Full-text

Sequence classification via large margin hidden Markov models

Data Mining and Knowledge Discovery ◽

10.1007/s10618-010-0206-6 ◽

2010 ◽

Vol 23 (2) ◽

pp. 322-344 ◽

Cited By ~ 6

Author(s):

Minyoung Kim ◽

Vladimir Pavlovic

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Sequence Classification ◽

Large Margin

Download Full-text

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

Discrete Dynamics in Nature and Society ◽

10.1155/2020/3915036 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Fengcai Qiao ◽

Xin Zhang ◽

Jinsheng Deng

Keyword(s):

Markov Model ◽

Large Scale ◽

Markov Models ◽

Modern Society ◽

Classification Problem ◽

Frequent Subgraph Mining ◽

Digital History ◽

Social Unrest ◽

The Social ◽

Frequent Subgraph

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%.

Download Full-text