GDELT数据网络化挖掘与国际关系分析

21世纪以来的国际关系错综复杂、瞬息万变,给世界的经济、安全、外交等带来了深刻变化。这些变化对中国的内外政策产生了重大影响。全面及时地分析国际关系及其变化特征,对于中国的经济和外交发展规划具有重要参考价值。国际关系研究具有复杂性、及时性、时空性等特点,迫切需要时空大数据分析技术为其提供新的思路和技术手段。大众媒体如报纸、广播等记录着世界上发生的各种各样的事件,蕴含着丰富的信息,相对于记录个人活动的社交媒体数据,其更加适合于对人类社会进行大规模和长时间的分析。GDELT是一个免费开放的新闻数据库,它实时监测世界上印刷、广播、网络媒体中的新闻,对其进行文本分析并提取出人物、地点、组织和事件等关键信息。本文利用复杂网络的理论和方法对GDELT进行网络化挖掘并进一步分析国家关系。首先利用该数据构建国家交互网络,然后通过网络特征统计分析国家之间的交互关系,最后探测国家冲突事件交互网络的时序变化。研究发现：① 国家交互网络具有无标度特性,网络连接在整体和局部上都呈现出不均匀性,少数国家与其他国家有大量交互,大多数国家与其他国家的交互很少;一个国家与少数国家有大量交互,而与大多数国家的交互很少。② 国家冲突事件交互网络的突然变化往往对应一些重大事件。本文的研究可以为大数据时代的国际关系探索提供一个新的视角,同时也为新闻媒体数据的分析提供参考。

Networked Mining of GDELT and International Relations Analysis

The international relations are intricate and ever-changing since the 21^st century, and have brought profound changes to the world's economy, security, and diplomacy. These changes have had a major impact on China's internal and external policies. A comprehensive and timely analysis of international relations and its changing characteristics has important reference value for China's economic and diplomatic development planning. The analysis of international relations has spatio-temporal characteristics, and it needs real-time processing. Thus, it needs to introduce the methods of spatio-temporal big data analysis to analyze international relations. Traditional mass media such as news, radio, etc. record all kinds of events happening in the world. It contains a wealth of information. Compared with social media data recording personal activities, it is more suitable for large-scale and long-term analysis of human society. The Global Database of Events Language, and Tone (GDELT) is a free and open news database which monitors news from print, broadcast, and online media in the world, analyzes the texts and extracts the key information such as people, place, organization, and event. This paper researches the network characteristics of GDELT based on theory of complex network and further analyze the relations between countries. Firstly, this paper constructs national interaction networks using GDELT, then analyze the interaction relationship between countries through network characteristic statistics, and finally detect the time series changes of the national conflict event interaction network. The results show that: (1)The National interaction network has scale-free characteristics, the interaction between countries is unevenly distributed from a global and local perspective. Very few countries have lots of interactions while most countries have very few interactions, and one country has lots of interactions with a few countries while a few interactions with most countries. (2) Sudden changes in the national interaction network of conflict events often indicates some significant national conflict events. This paper can provide a new perspective for the exploration of international relations and a reference for the analysis of news media in the era of big data.