A Dataset for the Detection of Dehumanizing Language (2024)

Abstract: 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 as well as automatic classification of dehumanization patterns. Both data sets will be publicly released.

## introduction dehumanization, the act of depicting someone as less than human, can be seen in many different examples, such as against african americans @xcite , arabs @xcite as well as between israelis and palestinians @xcite . dehumanization can range from blatant to subtle forms of varying degrees @xcite , making automated, general detection difficult. @xcite present one of the first computational works on dehumanization through explicit feature engineering, using lexicon and word embedding based approaches to detect dehumanizing associations across several years in a new york times corpus. outside of this, there is little computational work on dehumanization. we believe that the lack of work can be attributed to a vague general definition of dehumanization and a pronounced focus on content moderation, rather than the underlying processes of hateful content. additionally, we notice a lack of data sets specializing on dehumanization. while similar data, such as social media hate speech data @xcite @xcite , exists, these do not capture the specifics of dehumanization. hate speech and dehumanization differ in the sense that hate speech is a surface phenomenon, representing the observable aspects of hateful content, whereas dehumanization describes the underlying attitude for certain types of hate speech. as a result, we wish to provide two dehumanization focused data sets to allow work on general identification and detection of dehumanization. both data sets are in english and collected from the opensubtitles @xcite as well as the common crawl foot_0 corpora. one data set consists of a larger, unlabelled corpus, while the other is an evaluation set consisting of human annotated samples, labelled by two independent annotators. both data sets were extracted using keywords, which include target groups from ethnic, religious and sexual backgrounds, as well as common animal metaphor keywords and moral disgust terms from the moral foundations dictionary foot_1 @xcite . for dehumanization patterns, we limit ourselves to patterns inspired by @xcite and @xcite , where a sample is considered dehumanizing if it contains at least one of the following categories: negative evaluation of a target group, denial of agency, moral disgust, animal metaphors, objectification. animal metaphors and objectification specifically relate to a human being compared to an animal or object with the intent to cause harm. trigger warning: this paper contains examples of hateful content that some may find upsetting. ## related work since computational work on dehumanization is sparse, we focus on related dehumanization research and other annotation efforts in fields such as hate speech detection. kteily and landry (2022) provide an overview of current trends and chal-lenges regarding dehumanization. @xcite focus on the use of the nrc-vad lexicon @xcite , which features 20,000 english keywords, rated by annotators based on their associated valence, dominance and arousal in the range of 0 to 1. valence, in particular, describes the evaluation of an event or concept and assigns it a value, ranging from unpleasant to pleasant @xcite . @xcite hypothesise that low valence is an indication of potential dehumanization in the form of a negative evaluation of a target group, while low dominance suggests dehumanization in the form of denial of agency. these, together with word embeddings made out of combining several keywords for moral disgust and vermin metaphors, are leveraged to identify dehumanized target groups. examining hate speech data sets, @xcite focus on explainable hate speech detection, aiming to increase the interpretability of hate speech detection models. @xcite provide a benchmark that not only tries to identify hate speech, but also expects generative models to be able to intervene in hateful discussions using automatically generated responses. for automated abuse detection, @xcite provides an overview for several techniques and methods that are commonly employed. transformer based models have shown particular promise in hate speech detection. an example is hatebert @xcite , a bert model trained from the ground up on hate speech data, outperforming the standard bert model on the detection of hate speech. ## discussion as seen in we chose to label these as not sure, as these do not directly target anyone with the intent to cause harm, but rather talk about hypothetical scenarios of dehumanization. in cases like these it was difficult to argue for or against dehumanization, since the intent to cause harm is not immediately clear. furthermore, the labelling process revealed several cases that highlight the requirement for specific domain knowledge to be able to accurately assess if someone is being dehumanized. take the following example: newslime is the major reason californians are making a mass exodus from the woke state. [...] newslime is a white obammy. without knowing about the then governor of california, gavin newsom, it would be difficult to understand that he is being compared to slime, as newslime could also refer to someones real name. these cases showcase that it can be very difficult to detect dehumanization without having any kind of domain or context knowledge at hand and hints towards the direction that models may have to go to be able to perform effective detection. ## conclusions due to the ever evolving nature of dehumanization and abuse in general, automated detection methods stand before a significant challenge. we hope that by curating a dehumanization focused data set, we provide enough incentive for others to start exploring potential ways of developing computa-tional dehumanization methods and tackle the fight against online abuse.