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The goal of this work is to detect "gender biases" in the communication of users of Subreddits on the platform Reddit. The analysis is carried out for eleven selected Subreddits. Furthermore, an attempt is made to identify different user types with the help of a k-means clustering and also to analyze "gender biases" in their communication. Based on the aggregated datasets, fasttext Word Embedding models are trained to identify terms that show high semantic relatedness in terms of cosine similarity of their word vectors with selected feminine and masculine terms.
To this end, the terms are analyzed for sentiment using the NRC-VAD Lexicon and tested for statistically significant differences. In addition, the Word Embedding Association Test (WEAT) is performed to check for subliminal associations. In relation to the considered text corpus, it is essentially observed that women are frequently associated with adjectives that associate them with appearances,
childbearing abilities or adaptability also in relation to the family. In contrast, men are associated with and measured by adjectives that refer to their prestige, strengths and weaknesses, career or physical characteristics.