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Multilingual Fairness LREC

Data and code repository to evaluate multilingual fairness for hate speech detection for the LREC 2020 paper Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition.

Data

Email xiaolei.huang@memphis.edu to get anonymized text corpus.

  • Languages
    • English
    • Italian
    • Polish
    • Portugese
    • Spanish
    • Hinglish (to be added)
    • More languages in the future
  1. Short Description This multilingual dataset convers five languages for the hate speech detection task. Each dataset contains five author level demographic attributes: age, country, gender and race/ethinicity. We binarize each attribute for simplifying analysis and experiments below. For the non-binary attribute labels, we are open to share for research community upon request.
  • Age: if larger than the median value within each language data;
  • Country: first identify a country value has the major users, then binarize the attribute if user is from the country value;
  • Gender: male or female;
  • Race/Ethnicity: we first extract the attributes using four American major race groups, White, Black, Hispanic/Latino and Asian; we then tentatively binarize the race label into white and other groups.

How to Run

  • Install the following:;

    • Install conda;
    • Install Python packages:
      • With conda: conda env create -f environment.yml, then conda activate lrec2020
      • With pip: pip install -r requirements.txt.
  • Process pre-trained word embeddings;

  • Run Python scripts

    • Run analysis on the dataset:
      • cd ./analysis
      • python predictability.py
    • Preprocess the data: python preprocess.py
    • Test logistic regression classifier: python lr.py;
    • Test RNN classifier: python rnn.py;
    • Test CNN classifier: python cnn.py;
    • Test BERT classifier: python bert.py.

Contact

To request non-binary demographic labels or if you have any issues, please email xiaolei.huang@colorado.edu.

Citation

If you use our corpus in your publication, please kindly cite this paper:

@inproceedings{huang2020-lrec,
    title = "Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition",
    author = "Huang, Xiaolei  and
      Linzi, Xing  and
      Dernoncourt, Franck  and
      Paul, Michael J.",
    booktitle = "Proceedings of the Twelveth International Conference on Language Resources and Evaluation ({LREC} 2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://arxiv.org/pdf/2002.10361.pdf",
    abstract = "Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this work, we assemble and publish a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. We evaluate the inferred demographic labels with a crowdsourcing platform, Figure Eight. To examine factors that can cause biases, we take an empirical analysis of demographic predictability on the English corpus. We measure the performance of four popular document classifiers and evaluate the fairness and bias of the baseline classifiers on the author-level demographic attributes.",
}

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Data and code repository of " Multilingual Fairness Evaluation for Hate Speech Detection ". LREC 2020.

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