Update README.md

README.md

@@ -66,11 +66,11 @@ This endeavor involved the integration of labeled document sets from seminal stu
 Leite et al. (2020), and Vargas et al. (2022). To ensure the highest degree of consistency and compatibility within our dataset, 
 we adhered to stringent guidelines for text integration, detailed as follows:
 
-1. Fortuna et al. (2019): This study presented a dataset of 5,670 tweets, each annotated by three independent evaluators to ascertain the presence or absence of hate speech. In our integration process, we adopted a simple majority-voting mechanism to classify each document, ensuring a consistent approach to hate speech identification across the dataset.
+1. **Fortuna et al. (2019)**: This study presented a dataset of 5,670 tweets, each annotated by three independent evaluators to ascertain the presence or absence of hate speech. In our integration process, we adopted a simple majority-voting mechanism to classify each document, ensuring a consistent approach to hate speech identification across the dataset.
 
-2. Leite et al. (2020): The dataset from this research encompassed 21,000 tweets, annotated by 129 volunteers. Each tweet was reviewed by three different assessors. The study identified six categories of toxic speech, namely: (i) homophobia, (ii) racism, (iii) xenophobia, (iv) offensive language, (v) obscene language, and (vi) misogyny. In aligning with our operational definition of hate speech, we chose to exclude texts that solely fell under the categories of offensive and/or obscene language. Consistent with our methodology, a straightforward majority-voting process was utilized for the classification of these texts.
+2. **Leite et al. (2020)**: The dataset from this research encompassed 21,000 tweets, annotated by 129 volunteers. Each tweet was reviewed by three different assessors. The study identified six categories of toxic speech, namely: (i) homophobia, (ii) racism, (iii) xenophobia, (iv) offensive language, (v) obscene language, and (vi) misogyny. In aligning with our operational definition of hate speech, we chose to exclude texts that solely fell under the categories of offensive and/or obscene language. Consistent with our methodology, a straightforward majority-voting process was utilized for the classification of these texts.
 
-3. Vargas et al. (2022): This research involved a compilation of 7,000 comments sourced from Instagram, each labeled by a trio of annotators. These data had already been subjected to a simple majority-voting classification, thereby obviating the need for us to apply additional text classification protocols.
+3. **Vargas et al**. (2022): This research involved a compilation of 7,000 comments sourced from Instagram, each labeled by a trio of annotators. These data had already been subjected to a simple majority-voting classification, thereby obviating the need for us to apply additional text classification protocols.
 
 Through the application of these rigorous integration guidelines, we have succeeded in establishing a robust, unified database that stands as a valuable resource for the development and refinement of automatic hate speech detection systems in the Portuguese language.
 
@@ -136,3 +136,10 @@ Table 3 provides a detailed analysis of the dataset, delineating the data volume
 
 # Acknowledge
 The TuPy project is the result of the development of Felipe Oliveira's thesis and the work of several collaborators. This project is financed by the Federal University of Rio de Janeiro ([UFRJ](https://ufrj.br/)) and the Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering ([COPPE](https://coppe.ufrj.br/)).
+
+# References 
+[1] P. Fortuna, J. Rocha Da Silva, J. Soler-Company, L. Wanner, and S. Nunes, “A Hierarchically-Labeled Portuguese Hate Speech Dataset,” 2019. [Online]. Available: https://github.com/t-davidson/hate-s
+
+[2] J. A. Leite, D. F. Silva, K. Bontcheva, and C. Scarton, “Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis,” Oct. 2020, [Online]. Available: http://arxiv.org/abs/2010.04543
+
+[3] F. Vargas, I. Carvalho, F. Góes, T. A. S. Pardo, and F. Benevenuto, “HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection,” 2022. [Online]. Available: https://aclanthology.org/2022.lrec-1.777/