{'fraction_non_alphanumeric': 0.018913971934106162, 'fraction_numerical': 0.0012202562538133007, 'mean_word_length': 5.3565891472868215, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
How Machine Learning is Changing e-Government

Big Data is, clearly, an integral part of modern information societies. A vast amount of data is daily produced and it is estimated that, for the years to come, this number will grow dramatically. In an effort to transform the hidden information in this ocean of data into a useful one, the use of advanced technologies, such as Machine Learning, is deemed appropriate. Machine Learning is a technology that can handle Big Data classification for statistical or even more complex purposes, such as decision making. This fits perfectly with the scope of the new generation of government, Government 3.0, which explores all the new opportunities to tackle any challenge faced by contemporary societies, by utilizing new technologies for data-driven decision making. Boosted by the opportunities, Machine Learning can facilitate more and more governments participate in the development of such applications in different governmental domains. But is the Machine Learning only beneficial for public sectors? Although there is a huge number of researches in the literature related to Machine Learning applications, there is lack of a comprehensive study focusing on the usage of this technology within governmental applications. The current paper moves towards this research question, by conducting a comprehensive analysis of the use of Machine Learning by governments. Through the analysis, quite interesting findings have been identified, containing both benefits and barriers from the public sectors' perspective, pinpointing a wide adoption of Machine Learning approaches in the public sector.