A Cancel Culture Corpus through the lens of Natural Language Processing

This repository contains the Cancel Culture Corpus from the associated paper by Justus-Jonas Erker, Catalina Goanta and Jerry Spanakis.

Abstract

Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective. This paper demonstrates how Natural Language Processing tasks can be derived from this previous work, underlying techniques on how cancel culture can be measured, identified and evaluated. As part of this paper, we introduce a first cancel culture data set with of over 2.3 million tweets and a framework to enlarge it further. We provide a detailed analysis of this data set and propose a set of features, based on various models including sentiment analysis and emotion detection that can help characterizing cancel culture.

Citation

For attribution in academic contexts, please cite this work as:

@InProceedings{erker-goanta-spanakis:2022:LATERAISSE,
  author    = {Erker, Justus-Jonas  and  Goanta, Catalina  and  Spanakis, Gerasimos},
  title     = {A Cancel Culture Corpus through the Lens of Natural Language Processing},
  booktitle      = {Proceedings of The First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference},
  month          = {June},
  year           = {2022},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {17--25},
  abstract  = {Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective. This paper demonstrates how Natural Language Processing tasks can be derived from this previous work, underlying techniques on how cancel culture can be measured, identified and evaluated. As part of this paper, we introduce a first cancel culture data set with of over 2.3 million tweets and a framework to enlarge it further. We provide a detailed analysis of this data set and propose a set of features, based on various models including sentiment analysis and emotion detection that can help characterizing cancel culture.},
  url       = {https://aclanthology.org/2022.lateraisse-1.3}
}