Abstract
Social unrest is a negative consequence of certain events and social factors that cause widespread dissatisfaction in society. We wanted to use the power of machine learning (Random Forests, Boosting, and Neural Networks) to try to explain and predict when huge social unrest events (Huge social unrest events are major social unrest events as recognized by Wikipedia page ‘List of incidents of civil unrest in the United States’) might unfold. We examined and found that the volume of news articles published with a negative sentiment grew after one such event - the death of Sandra Bland - and in other similar incidents where major civil unrest followed. We used news articles captured from Google’s GDELT (Global Database of Events, Language, and Tone) table at various timestamps as a medium to study the factors and events in society that lead to large scale unrest at both State and County levels in the United States of America. In being able to identify and predict social unrest at the county level, programs/applications can be deployed to counteract its adverse effects. This paper attempts to address this task of identifying, understanding, and predicting when social unrest might occur.
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Korolov, R., Lu, D., Wang, J., Zhou, G., Bonial, C., Voss, C., Kaplan, L., Wallace, W., Han, J., Ji, H.: On predicting social unrest using social media. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2016)
Mishler, A., Wonus, K., Chambers, W., Bloodgood, M.: Filtering tweets for social unrest. In : IEEE 11th International Conference on Semantic Computing (ICSC) (2017)
Xu, J., Lu, T.-C., Compton, R., Allen, D.: Civil unrest prediction: a tumblr-based exploration. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds.) SBP 2014. LNCS, vol. 8393, pp. 403–411. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05579-4_49
Korkmaz, G., Kuhlman, C.J., Marathe, A., Ramakrishnan, N., Vullikanti, A.: Forecasting social unrest using activity cascade. PLoS ONE 10(6), e0128879 (2015). https://doi.org/10.1371/journal.pone.0128879
Qiao, F., Li, P., Zhang, X., Ding, Z., Cheng, J., Wang, H.: Predicting social unrest events with hidden Markov models using GDELT. Discrete Dyn. Nat. Soc. 2017 (2015). https://www.hindawi.com/journals/ddns/2017/8180272/
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Galla, D., Burke, J. (2018). Predicting Social Unrest Using GDELT. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_8
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DOI: https://doi.org/10.1007/978-3-319-96133-0_8
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