Abstract
Having the ability to forecast civil unrest events, such as violent protests, is crucial because they can lead to severe violent conflict and social instabilities. Civil unrests are comprehensive consequences of multiple factors, which could be related to political, economic, cultural, and other types of historical events. Therefore, people naturally organize such historical data into time-series data and feed it into an RNN-like model to perform the forecasting. However, how to encode discrete historical information into a unified vector space is very important. Different events may have extensive and complex relationships in time, space, and participants. Traditional methods, such as collecting indicators of various fields as features, miss the vital correlation information between events. In this work, we propose a Graph Neural Network based model to learn the representation of correlated historical event information. By using the dates, events, participants, and locations as nodes, we construct an event graph so that the relationship between events can be expressed unambiguously. We organize date-node’s representations into time-series data and use an LSTM to predict if there will be a violent protest or demonstration in the next few days. In the experiments, we use historical events from Hong Kong to evaluate our system’s forecasting ability in 1-day, 2-day, and 3-day lead-time. Our system achieves recall rates of 0.85, 0.86, 0.88, and precision rates of 0.75, 0.77, 0.75, respectively. We also discussed the impact of longer prediction lead times, and external events in China Mainland, the United States, and the United Kingdom on the Hong Kong civil unrest event prediction.
Supported by the Sichuan Science and Technology Plan Project 2020YFG0009.
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References
Chen, P., Jatowt, A., Yoshikawa, M.: Towards future event prediction using graph-LSTM
Cortez, B., Carrera, B., Kim, Y.J., Jung, J.Y.: An architecture for emergency event prediction using LSTM recurrent neural networks. Expert Syst. Appl. 97, 315–324 (2018)
Dermisi, S., et al.: Social media, civil unrest and fallout for cities and hotels. Technical report. European Real Estate Society (ERES) (2017)
Duvenaud, D., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing System 2015, pp. 2224–2232 (2015)
Gerner, D.J., Schrodt, P.A., Francisco, R.A., Weddle, J.L.: Machine coding of event data using regional and international sources. Int. Stud. Quart. 38(1), 91–119 (1994)
Islam, M.K., Ahmed, M.M., Zamli, K.Z., Mehbub, S.: An online framework for civil unrest prediction using tweet stream based on tweet weight and event diffusion. J. Inf. Commun. Technol. 19(1), 65–101 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings. OpenReview.net (2017). https://openreview.net/forum?id=SJU4ayYgl
Korkmaz, G., Cadena, J., Kuhlman, C.J., Marathe, A., Vullikanti, A., Ramakrishnan, N.: Combining heterogeneous data sources for civil unrest forecasting. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 258–265 (2015)
Leetaru, K., Schrodt, P.A.: GDELT: global data on events, location, and tone, 1979–2012. In: ISA Annual Convention, vol. 2, pp. 1–49. Citeseer (2013)
Meng, L., Srihari, R.K.: Increasing lead time and granularity of civil unrest prediction through time series data (2019)
Muthiah, S., et al.: Planned protest modeling in news and social media. In: Twenty-Seventh IAAI Conference. Citeseer (2015)
Qiao, F., Li, P., Zhang, X., Ding, Z., Cheng, J., Wang, H.: Predicting social unrest events with hidden Markov models using GDELT. In: Discrete Dynamics in Nature and Society 2017 (2017)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Vaswani, A., et al.: Attention is all you need, pp. 6000–6010 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Vossen, P., et al.: Newsreader: using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news. Knowl.-Based Syst. 110, 60–85 (2016)
Zhao, L.: Event prediction in the big data era: a systematic survey. ACM Comput. Surv. 54(5) (2021). https://doi.org/10.1145/3450287
Zhu, C., Chen, M., Fan, C., Cheng, G., Zhan, Y.: Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks. In: AAAI (2021)
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Chen, Z., Wang, Y. (2021). Civil Unrest Event Forecasting Using Graphical and Sequential Neural Networks. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_16
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