This dataset is a collection of tweets that were posted in response to news articles from five specific Argentinean news outlets: Clarín, Infobae, La Nación, Perfil and Crónica, during the COVID-19 pandemic. The comments were analyzed for hate speech across eight different characteristics: against women, racist content, class hatred, against LGBTQ+ individuals, against physical appearance, against people with disabilities, against criminals, and for political reasons. All the data is in Spanish.

Each comment is labeled with the following variables

Label	Description
HATEFUL	Contains hate speech (HS)?
CALLS	If it is hateful, is this message calling to (possibly violent) action?
WOMEN	Is this against women?
LGBTI	Is this against LGBTI people?
RACISM	Is this a racist message?
CLASS	Is this a classist message?
POLITICS	Is this HS due to political ideology?
DISABLED	Is this HS against disabled people?
APPEARANCE	Is this HS against people due to their appearance? (e.g. fatshaming)
CRIMINAL	Is this HS against criminals or people in conflict with law?

There is an extra label CALLS, which represents whether a comment is a call to violent action or not.

The HATEFUL and CALLS labels are binarized by simple majority; the characteristic or category variables are put to 1 if at least one annotator marked it as such.

A raw, non-aggregated version of the dataset can be found at piuba-bigdata/contextualized_hate_speech_raw

Citation Information

@article{perez2022contextual,
  author  = {Pérez, Juan Manuel and Luque, Franco M. and Zayat, Demian and Kondratzky, Martín and Moro, Agustín and Serrati, Pablo Santiago and Zajac, Joaquín and Miguel, Paula and Debandi, Natalia and Gravano, Agustín and Cotik, Viviana},
  journal = {IEEE Access},
  title   = {Assessing the Impact of Contextual Information in Hate Speech Detection},
  year    = {2023},
  volume  = {11},
  number  = {},
  pages   = {30575-30590},
  doi     = {10.1109/ACCESS.2023.3258973}
}