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This abstract aims at exploring the methodological aspects of a research by the authors titled "Evolution of migration governance in Brazil: theoretical challenges, normative change and social echoes of xenophobic feelings", from early 2020. The empirical dimension of the research took advantage of algorithmic rountines of Python language code to leverage the retroactive collection of textual contents contained in Twitter repository. A total number of 2404 tweets were treated as secondary netnographic data. The selection criteria for the retrieval were both the time frame (january 2016 to january 2020), and the text expression "out Venezuelans" ("fora venezuelanos" in Portuguese). Although the sampling were not statistically representative of the broad public opinion in Brazil, the choice helped to convey the existence of a diffuse sense of xenophobia in Brazil. The analysis utilized techniques of natural language processing in Python and unsupervised machine learning algorithm word2vec, which provides a means to computationally learn semantical associations between words within a corpus of text by making use of vectorized representation of those words. As the research findings show, semantic context learnt through the parsing of text into the word2vec algorithm made possible to discern typologies of discourses either pro or against the presence of immigrants in Brazil.
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