Abstract:
Animals, objects, food, plants, and other non-human terms are commonly used as a source of metaphors to describe females, in formal and slang language. Comparing women to non-human items not only reflects cultural views that might conceptualize women as subordinates or in a lower position than humans, yet it conveys this degradation to the listeners. Moreover, the dehumanizing representation of females in the language normalizes the derogation and, even, encourages sexism and aggressiveness against women. Although dehumanization has been a popular research topic for decades, according to our knowledge no studies have linked the women dehumanizing language to the machine learning field. Therefore, we introduce our research work as one of the first attempts to create a tool for the automated detection of the dehumanizing depiction of females in English texts. We, also, present the first labeled dataset on the charted topic, which is used for training supervised machine learning algorithms to build an accurate classification model. The importance of this work is that it accomplishes the first step toward mitigating dehumanizing language against females.
