A Dataset for the Detection of Dehumanizing Language
This work addresses the need for data to study and detect dehumanization in text, which is important for social science and NLP researchers, but it is incremental as it focuses on dataset creation without novel methods.
The paper tackles the problem of detecting dehumanizing language by presenting two datasets: a large automatically collected corpus and a smaller manually annotated dataset, both combining political discourse and movie subtitles, enabling further analysis and classification.
Dehumanization is a mental process that enables the exclusion and ill treatment of a group of people. In this paper, we present two data sets of dehumanizing text, a large, automatically collected corpus and a smaller, manually annotated data set. Both data sets include a combination of political discourse and dialogue from movie subtitles. Our methods give us a broad and varied amount of dehumanization data to work with, enabling further exploratory analysis and automatic classification of dehumanization patterns. Both data sets will be publicly released.