Question: What does their case study demonstrate?
Evidence: Despite the American public's increasing acceptance of LGBTQ people and recent legal successes, LGBTQ individuals frequently remain the targets of hate and violence BIBREF0, BIBREF1, BIBREF2. At the core of this issue is dehumanization, “the act of perceiving or treating people as less than human” BIBREF3, a process that heavily contributes to extreme intergroup bias BIBREF4. Language is central to studying this phenomenon; like other forms of bias BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, dehumanizing attitudes are expressed through subtle linguistic manipulations, even in carefully-edited texts. It is crucial to understand the use of such linguistic signals in mainstream media, as the media's representation of marginalized social groups has far-reaching implications for social acceptance, policy, and safety.
While small-scale studies of dehumanization and media representation of marginalized communities provide valuable insights (e.g. BIBREF10), there exist no known large-scale analyses, likely due to difficulties in quantifying such a subjective and multidimensional psychological process. However, the ability to do large-scale analysis is crucial for understanding how dehumanizing attitudes have evolved over long periods of time. Furthermore, by being able to account for a greater amount of media discourse at once, large-scale techniques can provide a more complete view of the media environment to which the public is exposed.
Linguistics and computer science offer valuable methods and insights on which such large-scale techniques might be developed for the study of dehumanization. By leveraging more information about the contexts in which marginalized groups are discussed, computational linguistic methods not only enable large-scale study of a complex psychological phenomenon, but can even reveal linguistic variations and changes that are not easily identifiable through qualitative analysis alone.
In this work, we develop a computational linguistic framework for analyzing dehumanizing language, with a focus on lexical signals of dehumanization. Social psychologists have identified numerous components of dehumanization, such as negative evaluations of a target group, denial of agency, moral disgust, and likening members of a target group to non-human entities such as vermin. Drawing upon this rich body of literature, we first identify clear linguistic analogs for these components and propose several computational techniques to measure these linguistic correlates. We then apply this general framework to explore changing representations of LGBTQ groups in the New York Times over the span of three decades. We additionally use this lens of dehumanization to investigate differences in social meaning between the denotationally-similar labels gay and homosexual.
This paper aims to bridge the gaps between computational modeling, sociolinguistics, and dehumanization research with implications for several disciplines. In addition to enabling large-scale studies of dehumanizing language and media representation of marginalized social groups, these techniques can be built into systems that seek to capture both conscious and unconscious biases in text. Furthermore, this work has implications for improving machines' abilities to automatically detect hate speech and abusive language online, which are typically underpinned by dehumanizing language. Finally, our case study demonstrates that such computational analyses of discussions about marginalized groups can provide unique insights into language variation and change within sensitive sociopolitical contexts, and help us understand how people (and institutions) use language to express their ideologies and attitudes towards certain social groups.
Finally, our case study demonstrates that such computational analyses of discussions about marginalized groups can provide unique insights into language variation and change within sensitive sociopolitical contexts, and help us understand how people (and institutions) use language to express their ideologies and attitudes towards certain social groups.
Answer: Such computational analyses of discussions about marginalized groups can provide unique insights into language variation and change within sensitive sociopolitical contexts, and help them understand how people (and institutions) use language to express their ideologies and attitudes towards certain social groups.