Update README.md

README.md

@@ -47,9 +47,12 @@ dataset tailored for adept handling in both binary and multiclass classification
 
 ## Dataset creation
 
-To overcome the notable shortcomings in existing Portuguese repositories of hate speech instances, we present the TuPI dataset. Recognizing the importance of prior research in this domain and the absence of annotated datasets for automated hate speech detection, we propose consolidating this comprehensive dataset by integrating the discoveries from Fortuna et al. (2019); Leite et al. (2020); Vargas et al. (2022), alongside a new, proprietary dataset. 
+To overcome the notable shortcomings in existing Portuguese repositories of hate speech instances, we present the TuPI dataset. 
+Recognizing the importance of prior research in this domain and the absence of annotated datasets for automated hate speech detection, 
+we propose consolidating this comprehensive dataset by integrating the discoveries from [Fortuna et al. (2019)](https://aclanthology.org/W19-3510/); [Leite et al. (2020)](https://arxiv.org/abs/2010.04543); [Vargas et al. (2022)](https://aclanthology.org/2022.lrec-1.777/), 
+alongside a new, proprietary dataset. 
 Regarding the unpublished part of the TuPI dataset, we spent about seven months, from March 2023 to September 2023, building the corpus. We collaborated with a team of experts, including a linguist, a human rights lawyer, several behavior psychologists with master’s degrees, and NLP and machine learning researchers.
-A framework inspired by Vargas et al. (2022) and Fortuna (2017) was adhered to by establishing a stringent set of criteria for the selection of annotators, encompassing the following key attributes:
+A framework inspired by [Vargas et al. (2022)](https://github.com/franciellevargas/HateBR/tree/main) and [Fortuna (2017)](https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset) was adhered to by establishing a stringent set of criteria for the selection of annotators, encompassing the following key attributes:
 * Diverse political orientations, encompassing individuals from the right-wing, liberal, and far-left spectrums.
 *  A high level of academic attainment comprising individuals with master’s degrees, doctoral candidates, and holders of doctoral degrees.
 * Expertise in fields of study closely aligned with the focus and objectives of our research.
@@ -84,5 +87,6 @@ The categories used included ageism, aporophobia, body shame, capacitism, LGBTph
 It is important to note that a single tweet could fall under one or more of these categories.
 
 ## References 
-
-[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://www.statista.com/
+[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/