by Pratik Sachdeva, Renata Barreto, Claudia von Vacano, and Chris J. Kennedy

This repository provides the code to reproduce the analyses and figures developed in the paper Targeted Identity Group Prediction in Hate Speech Corpora by Sachdeva et al., published in 2022 at the 6th Workshop on Online Abuse and Harms (WOAH).

The past decade has seen an abundance of work seeking to detect, characterize, and measure online hate speech. A related, but less studied problem, is the detection of identity groups targeted by that hate speech. Predictive accuracy on this task can supplement additional analyses beyond hate speech detection, motivating its study. Using the Measuring Hate Speech corpus, which provided annotations for targeted identity groups, we created neural network models to perform multi-label binary prediction of identity groups targeted by a comment. Specifically, we studied 8 broad identity groups and 12 identity sub-groups within race and gender identity. We found that these networks exhibited good predictive performance, achieving ROC AUCs of greater than 0.9 and PR AUCs of greater than 0.7 on several identity groups. We validated their performance on HateCheck and Gab Hate Corpora, finding that predictive performance generalized in most settings. We additionally examined the performance of the model on comments targeting multiple identity groups. Our results demonstrate the feasibility of simultaneously identifying targeted groups in social media comments.

The code used for this paper are divided into two repositories. To train the target identity models, you will need the Tensorflow layers defined in the hate_measure repository. To run the scripts used to set up and train those models, run secondary analyses, and generate the figures, you will need the code in this repository.

The repository is divided into the following folders:

• figures: Jupyter notebooks used to generate the figures in the paper.
• hate_target: Contains the codebase used in scripts, analyses, and figure generation for this paper.
• notebooks: Contains supplementary Jupyter notebooks used in secondary analyses.
• scripts: Contains Python scripts used to train variants of the model, whose predictions were analyzed in the paper.

To run the code, first download this repository to your local machine. The easiest way to do this is to clone to code via SSH:

git clone [email protected]:dlab-projects/hate_target.git

Navigate to the cloned folder on your local machine. Then, create a new Anaconda environment with the environment.yml file:

conda env create -f environment.yml

Finally, install an editable version of this package using pip. Be sure to run the following command in the hate_target folder, where setup.py is visible:

pip install -e .

You should now have access to hate_target's functions as importable modules anywhere in your virtual environment.