Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Journal of Sociology and Ethnology
  4. Vol 4, Issue 7, 2022

Big Data-Based Analysis and Prediction of International Events

Download as PDF

DOI: 10.23977/jsoce.2022.040703 | Downloads: 86 | Views: 1067

Author(s)

Bingnan Wang 1

Affiliation(s)

1 College of National Security, People's Public Security University of China, Beijing 100038, China

Corresponding Author

Bingnan Wang

ABSTRACT

International event data can be seen as sensors that explain the interaction of countries. With the help of international event data, we can quantitatively analyze bilateral and even multilateral relations, which has very important reference value for stakeholders such as multinational companies and policy makers. This article explores the potential of the GDELT large database in the analysis and prediction of international event data. First, we introduced the GDELT database, data format and coding system of International Events Data Analysis. Second, we select all interaction events between China and other countries in the world from February 2020 to September 2021 to understand China's interaction with other countries and the trend of China's international evaluation through the analysis of conflict-cooperative events and media tone. Finally, we selected a more random "AvgTone" field for prediction, and proposed a prediction model based on the Encoder-Decoder Attention framework. After experiments, the model can still converge against data with a lot of noise, which proves the potential value of deep learning algorithms in international event data analysis.

KEYWORDS

Big data, International event data, Deep learning, Encoder-decoder attention, News media tone

CITE THIS PAPER

Bingnan Wang, Big Data-Based Analysis and Prediction of International Events. Journal of Sociology and Ethnology (2022) Vol. 4: 16-23. DOI: http://dx.doi.org/10.23977/jsoce.2022.040703.

REFERENCES

[1] Qingling Dong, "Big Data Security Situation Awareness and Conflict Prediction". Chinese Social Sciences, vol.06, no.2, pp.172-182, 2016.
[2] Peng Chen, Adam Jatowt, and Masatoshi Yoshikawa, "Conflict or Cooperation? Predicting Future Tendency of International Relations". in Proceedings of the 35th Annual Acm Symposium on Applied Computing, pp923–30, 2020.
[3] Menglan Ma et al., "Does Ideology Affect the Tone of International News Coverage?". International Conference on Behavioral, Economic, Socio-cultural Computing (BESC), pp1-5, 2017.
[4] Divyanshi Galla and James Burke, "Predicting Social Unrest Using GDELT". International Conference on Machine Learning and Data Mining in Pattern Recognition, pp103-16, Springer 2018.
[5] Noam Levin, Saleem Ali, and David Crandall, "Utilizing Remote Sensing and Big Data to Quantify Conflict Intensity: The Arab Spring as a Case Study". Applied Geography, vol94, no.3, pp1-17, 2018.
[6] Yilmaz, Philip Schrodt, and Deborah Gerner, "Conflict and Mediation Event Observations (CAMEO): An Event Data Framework for a Post-Cold War World". Security and Conflict Management, vol. 4, pp287-304, 2008.
[7] S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory," Neural Computation 9, vol.12, no. 8, pp1735–80, 1997.
[8] Minh-Thang Luong, Hieu Pham, and Christopher D. Manning, "Effective Approaches to Attention-Based Neural Machine Translation". ArXiv: 1508. 04025 [Cs], 2015.

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.