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README.md
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@@ -20,10 +20,10 @@ We are a multidisciplinary research team based at the Universidad de Buenos Aire
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Our research aims to study and implement methodologies for the study of social exclusions, from an interdisciplinary approach, by applying research techniques focused on the analysis of large volumes of data. We mainly work on textual sources, using different techniques and strategies from artificial intelligence, such as text mining and natural language processing (NLP) and machine learning, including deep neural networks (deep learning), multivariate statistical methods and data visualization.
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## Published Work
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- Pérez, J. M
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- Cotik, V
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Our research aims to study and implement methodologies for the study of social exclusions, from an interdisciplinary approach, by applying research techniques focused on the analysis of large volumes of data. We mainly work on textual sources, using different techniques and strategies from artificial intelligence, such as text mining and natural language processing (NLP) and machine learning, including deep neural networks (deep learning), multivariate statistical methods and data visualization.
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We are pleased to present here the results of a study on hate speech detection in social networks, from an interdisciplinary perspective, addressing hate speech both quantitative and qualitatively, during the COVID-19 pandemic time frame.
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## Published Work
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- Pérez, J. M.; Luque, F.; Zayat, D.; Kondratzky, M.; Moro, A.; Serrati, P.; Zajac, J.; Miguel, P.; Debandi, N.; Gravano, A. & Cotik, V. (2022). [Assessing the impact of contextual information in hate speech detection](https://arxiv.org/pdf/2210.00465.pdf). arXiv preprint arXiv:2210.00465. (TBP IEEE Access 2023)
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- Cotik, V.; Debandi, N.; Luque, F.; Miguel, P.; Moro, A.; Pérez, J. M.; Serrati, P.; Zajac, J. & Zayat, D. (2020). [A study of Hate Speech in Social Media during the COVID-19 outbreak](https://openreview.net/pdf?id=01eOESDhbSW).
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