{'fraction_non_alphanumeric': 0.02495201535508637, 'fraction_numerical': 0.007677543186180422, 'mean_word_length': 5.60126582278481, '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}}
Cross-national analysis of global security discourse using word embeddings

This study conducts a systematic investigation of discursive dynamics in the context of policy deliberations on international peace and security, by performing cutting-edge text analysis on the entire body of meeting records of the Security Council of the United Nations for the past quarter of a century (1994-2019). Focusing on one of the most consequential notions for the council’s policy-making, "threat to the peace," it employs an unsupervised machine learning model, broadly termed "word embedding," to analyze how this notion has been discussed by relevant members of the council, especially its five permanent members, during the period under investigation. The study reveals persistent patterns of cross-national convergence and divergence in security discourse, including, most notably, a considerable degree of correlation in how to conceive international threats found between the close-knit Western allies in the council and their Russian counterpart.