  The development of theories and techniques for big data analytics offers
tremendous flexibility for investigating large-scale events and patterns that
emerge over space and time. In this research, we utilize a unique open-access
dataset "The Global Data on Events, Location and Tone" (GDELT) to model the
image of China in mass media, specifically, how China has related to the rest
of the world and how this connection has evolved upon time based on an
autoregressive integrated moving average (ARIMA) model. The results of this
research contribute both in methodological and empirical perspectives: We
examined the effectiveness of time series models in predicting trends in
long-term mass media data. In addition, we identified various types of
connection strength patterns between China and its top 15 related countries.
This study generates valuable input to interpret China's diplomatic and
regional relations based on mass media data, as well as providing
methodological references for investigating international relations in other
countries and regions in the big data era.
