Modeling Inter-country Connection from Geotagged News Reports: A Time-Series Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10387)

Abstract

The rapid development of big data techniques provides growing opportunities to investigate large-scale events that emerge over space and time. This research utilizes a unique open-access dataset, “The Global Data on Events, Location and Tone” (GDELT), to model how China has connected to the rest of the world, as well as predicting how this connection may evolve over time based on an autoregressive integrated moving average (ARIMA) model. Methodologically, we examined the effectiveness of traditional time series models in predicting trends in long-term mass media data. Empirically, we identified various types of ARIMA models to depict the connection patterns between China and its top 15 related countries. This study demonstrates the power of applying GDELT and big data analytics to investigate informative patterns for interdisciplinary researchers, as well as provides valuable references to interpret regional patterns and international relations in the age of instant access.

Keywords

Time series analysis ARIMA Inter-country relations Mass media events GDELT 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of GeographyTexas State UniversitySan MarcosUSA

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