论文标题
使用机器学习模型在部分自由国家中解释互联网与民主之间的关系
Explaining the Relationship between Internet and Democracy in Partly Free Countries Using Machine Learning Models
论文作者
论文摘要
先前的研究就民主与互联网之间的关系提供了各种解释。但是，这些研究中的大多数都集中在地区，特定国家或专制政权上。没有研究研究自由之家定义的部分自由国家的互联网影响。此外，关于在线审查制度对民主发展，停滞或衰落的影响知之甚少。借助国际电信联盟，自由之家和世界银行数据库，并使用机器学习方法，这项研究揭示了互联网对部分自由国家民主化的影响。研究结果表明，互联网渗透和在线审查制度都对民主分数产生负面影响，并且互联网对民主分数的影响是由在线审查制定的。此外，随机森林的结果表明，在线审查制度是最重要的变量，其次是治理指数和民主分数教育。各种机器学习模型的比较表明，最好的预测模型是具有92％精度的175-树随机模型。同样，这项研究可能会帮助“ IT专业人员”看到他们的重要作用，不仅在技术领域，而且在民主化以及它与社会科学之间的距离方面也是如此。
Previous studies have offered a variety of explanations on the relationship between democracy and the internet. However, most of these studies concentrate on regions, specific states or authoritarian regimes. No study has investigated the influence of the internet in partly free countries defined by the Freedom House. Moreover, very little is known about the effects of online censorship on the development, stagnation, or decline of democracy. Drawing upon the International Telecommunication Union, Freedom House, and World Bank databases and using machine learning methods, this study sheds new light on the effects of the internet on democratization in partly free countries. The findings suggest that internet penetration and online censorship both have a negative impact on democracy scores and the internet's effect on democracy scores is conditioned by online censorship. Moreover, results from random forest suggest that online censorship is the most important variable followed by governance index and education on democracy scores. The comparison of the various machine learning models reveals that the best predicting model is the 175-tree random forest model which has 92% accuracy. Also, this study might help "IT professionals" to see their important role not only in the technical fields but also in society in terms of democratization and how close IT is to social sciences.
