  Social bias in language - towards genders, ethnicities, ages, and other
social groups - poses a problem with ethical impact for many NLP applications.
Recent research has shown that machine learning models trained on respective
data may not only adopt, but even amplify the bias. So far, however, little
attention has been paid to bias in computational argumentation. In this paper,
we study the existence of social biases in large English debate portals. In
particular, we train word embedding models on portal-specific corpora and
systematically evaluate their bias using WEAT, an existing metric to measure
bias in word embeddings. In a word co-occurrence analysis, we then investigate
causes of bias. The results suggest that all tested debate corpora contain
unbalanced and biased data, mostly in favor of male people with
European-American names. Our empirical insights contribute towards an
understanding of bias in argumentative data sources.
