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Previous studies have offered a variety of explanations on the relationshipbetween democracy and the internet. However, most of these studies concentrateon regions, specific states or authoritarian regimes. No study has investigatedthe influence of the internet in partly free countries defined by the FreedomHouse. Moreover, very little is known about the effects of online censorship onthe development, stagnation, or decline of democracy. Drawing upon theInternational Telecommunication Union, Freedom House, and World Bank databasesand using machine learning methods, this study sheds new light on the effectsof the internet on democratization in partly free countries. The findingssuggest that internet penetration and online censorship both have a negativeimpact on democracy scores and the internet's effect on democracy scores isconditioned by online censorship. Moreover, results from random forest suggestthat online censorship is the most important variable followed by governanceindex and education on democracy scores. The comparison of the various machinelearning models reveals that the best predicting model is the 175-tree randomforest model which has 92% accuracy. Also, this study might help "ITprofessionals" to see their important role not only in the technical fields butalso in society in terms of democratization and how close IT is to socialsciences.

Explaining the Relationship between Internet and Democracy in Partly Free Countries Using Machine Learning Models