Computational Analysis of Dehumanization of Ukrainians on R ussian Social Media (2024)

Abstract: Dehumanization is a pernicious process of denying some or all attributes of humanness to the target group. It is frequently cited as a common hallmark of incitement to commit genocide. The international security landscape has seen a dramatic shift following the 2022 Russian invasion of Ukraine. This, coupled with recent developments in the conceptualization of dehumanization, necessitates the creation of new techniques for analyzing and detecting this extreme violence-related phenomenon on a large scale. Our project pioneers the development of a detection system for instances of dehumanization. To achieve this, we collected the entire posting history of the most popular bloggers on Russian Telegram and tested classical machine learning, deep learning, and zero-shot learning approaches to explore and detect the dehumanizing rhetoric. We found that the transformer-based method for entity extraction SpERT shows a promising result of F 1 = 0.85 for binary classification. The proposed methods can be built into the systems of anticipatory governance, contribute to the collection of evidence of genocidal intent in the Russian invasion of Ukraine, and pave the way for large-scale studies of dehumanizing language. This paper contains references to language that some readers may find offensive.

## introduction dehumanization has been frequently proposed as a mechanism that mitigates or eliminates moral concern about cruel behavior, thus playing a crucial role in war, genocide, and other forms of extreme violence @xcite . recent research @xcite @xcite focuses on more subtle forms of dehumanization; those are considered both a precursor and a consequence of discrimination, violence, and other forms of day-to-day abuse outside of the context of armed conflicts. this shift in focus resulted in a simultaneously more nuanced and broad definition of dehumanization, inviting new approaches to operationalization. multiple investigations @xcite have been conducted regarding the 2022 russian invasion of ukraine to determine if the russian federation is responsible for violating the genocide convention foot_0 . central to these inquiries is the role of dehumanizing rhetoric in the direct and public encouragement of genocide. according to these reports, russian officials and state media repeatedly described ukrainians as subhuman ("bestial," "zombified"), contaminated or sick ("filth," "disorder"), or existential threats and the epitome of evil ("hitler youth," "third reich," "nazi"), rendering them legitimate or necessary targets for destruction. hence, detecting the dehumanizing rhetoric at scale within this particular context can provide comprehensive evidence for further inquiries, as well as empirically support or challenge the assumptions of existing dehumanization frameworks. ## background the concept of dehumanization has developed through cross-disciplinary conversations, integrating perspectives from various fields such as philosophy, psychology, sociology, and more. in the field of social psychology, @xcite investigated how people can psychologically detach themselves from others, viewing them as less than human, which can result in violence, bias, and discrimination. in sociology and critical theory, dehumanization was first scrutinized in relation to power dynamics, social disparities, and oppressive structures. academics and thinkers such as @xcite , @xcite , and @xcite examined how dehumanization contributes to the marginalization, subjugation, and violence in sce-narios of colonialism, totalitarianism, and genocide. dehumanization is often seen as a key stage leading to genocide 2 . however, @xcite argues that dehumanization is not just a precursor but is intertwined throughout the entire genocidal process. our research shows that the temporal change of dehumanizing rhetoric on russian telegram conforms with this view. ## definition and operationalization dehumanization is commonly defined as the denial of humanness to others @xcite . currently accepted frameworks mainly differ in understanding of "humanness" and of the ways in which this denial is taking place. @xcite defines dehumanization as denying an individual both "identity" and "community"; @xcite introduces "moral exclusion" as an extension. @xcite argues that dehumanization relaxes moral self-sanctions and prevents self-condemnation. harris and @xcite relate dehumanization to mental-state attribution, suggesting that dehumanized groups are perceived as having fewer mental states. this aligns with the mind perception theory by @xcite , which categorizes perceptions into two dimensions: agency and experience. the integrative review on dehumanization by @xcite proposes two distinct senses of humanness that can be denied in order to dehumanize persons or groups: human uniqueness (uh) and human nature (nh). according to haslam, the line dividing people from the related category of animals is defined by traits that are "uniquely human" (uh). refined emotions, higher-order cognition, and language can all be considered uniquely human. human-nature attributes (nh) are those that characterize humans in general and include emotional responsiveness, interpersonal warmth, openness, and emotional depth. building on that, @xcite proposed two corresponding types of dehumanization: animalistic dehumanization, in which people or groups are thought to have fewer characteristics that make them uniquely human (and are perceived as vermin, animals, or disease), and mechanistic dehumanization, in which people or groups are thought to have fewer characteristics that describe people in general (and are perceived 2 http://genocidewatch.net/genocide-2/8-stage s-of-genocide/ as automata or objects). @xcite proposed the mixed model of dehumanization to address existing variability in the literature on dehumanization. this model is informed by framework by @xcite ; it contains four quadrants, formed by the level of human nature and human uniqueness attribution. we found this framework consistent with the most recent empirical evidence found in historical documents @xcite . genocide researchers highlight the limitations of using dehumanization as an early warning sign for atrocities. @xcite introduced toxification as a more precise indicator. this concept goes beyond viewing victims as merely non-human and suggests that perpetrators see eradicating victims as essential for their survival, for two main reasons: victims are "toxic to the ideal" (threatening beliefs) or "toxic to the self" (posing harm). drawing from from @xcite , @xcite , and @xcite , we define dehumanization as the representation of the target group as existentially threatening and/or morally deficient by blatantly or subtly manipulating the features of its human uniqueness (including relevant elements in agency and competence) and/or human nature (including relevant elements in experience and warmth). figure 1 summarizes all types with corresponding metaphors. we chose the representation of ukrainians in russian telegram as the target of our research. below are some common blatantly dehumanizing metaphors used towards ukrainians broken into types of dehumanization. of type ↓ uh ↑ nh: укропитеки 3 , свинорез 4 , бандерлоги 5 ; of type ↓ uh ↓ nh: расходный материал 6 , майданутые 7 , горящее сало foot_1 , and of type ↑ uh 3 [ukropiteki] -a derogatory term, combining "ukro" for "ukrainian" and "piteki," which refers to early hominids 4 [svinorez] -"pig slaughter," implying that ukrainians are similar to pigs 5 [banderlogi] -a play on kipling's monkeys "bandarlog" and "bandera" (ukrainian nationalist and the leader of the ukrainian insurgent army) 6 [raskhodnyy material] -"expendable material" 7 [maidanutye] -the term is derived from "maidan," the center of the euromaidan protests in 2013-2014. the ending "-nutye" is common in russian slang words meaning "crazy" or "nuts". thus, the word can be translated as mentally ill with maidan. in the case of укропитеки 3 , by being likened to pre-humans, ukrainians are shown as lacking competence (dehumanized along the uh axis). desires or experience are not denied, so nh axis position is unaffected; thus, we assign the label ↓ uh ↑ nh. the укронацики 9 metaphor demonizes ukrainians and shapes an image of the epitome of evil, exaggerating competence and agency but reducing perceived warmth, affect, and shared human experience; thus we assign the label ↑ uh ↓ nh. we treat dehumanization signals as additive. therefore, compound words like свинорейх foot_5 , which consist of dehumanizing metaphors of ↓ uh ↑ nh and ↑ uh ↓ nh, are considered ↓ uh ↓ nh. table a .2 outlines a detailed description. ## related computational work to the best of our knowledge, the first and only computational analysis framework focusing on dehumanization was proposed by @xcite . it was applied to the analysis of discussions of lgbtq people in the new york times from 1986 to 2015. the authors identified linguistic correlates of salient components of dehumanization (negative evaluation, denial of agency, and metaphors of moral disgust and vermin) and then analyzed linguistic variation and change in discourses surrounding the chosen marginalized group. in the study on dehumanization toward immigrants, @xcite attempted to evaluate the psychology of dehumanizers through language patterns, hypothesizing that three language dimensions reflect those who tend to dehumanize immigrants: (i) prevalence of impersonal pronouns, (ii) use of power words (e.g., "pitiful," "victim," "weak"), and (iii) emotion terms, evaluated through the affect category in liwc @xcite . the study of @xcite identifies dehumanizing metaphors by measuring the likelihood of a word denoting foreigners being related to a number of well-known dehumanizing metaphors (like "animals" and "cargo") in immigration-related sentences. the approach is best suited for the research setting where exhaustive lists of the considered metaphors are available. work by @xcite is informed by @xcite ; it presents a knowledge graph schema, dataset, and transformer-based nlp model spert to identify and represent indicators of moral disengagement and dehumanization in text. they define the multi-attribute knowledge graph extraction task as predicting the set of entities, the set of relations over entities, and the set of attributes over entities in a given text span. among other indicators, they detect dehumanization based on the cumulative semantics of these attributes, entities, and relationships. in the study of nazi propaganda documents, @xcite analyzed the prevalence of agency and experience mental state terms used when referring to jews in nazi germany, building on the moral disengagement theory by @xcite and mind perception theory introduced by @xcite . ## research setting existing solutions by @xcite ; @xcite have not yet been tested on cross-domain tasks. computational analysis of dehumanization in the context of extreme violence @xcite so far relied only on dictionary and lexicon-based approaches, and few dehumanization frameworks have been tested. moreover, state-of-the-art large language models (llms) showing promising results across diverse domains were not yet tested for this task. ## dataset telegram is beneficial for our task due to (i) broad geographical scope and growth; (ii) being a primary messenger for most russians @xcite ; (iii) state role in decentralized content discovery: the russian state uses the centralized endorsement by influencers, further amplifying their messages @xcite . ## temporal dynamics of dehumanization to investigate how types of dehumanization evolved over time, we used spert in the multiclass setting to detect instances of dehumanization throughout the initially collected telegram dataset. we calculated the representative sample size for each period to assess the dynamics of dehumanization by type for 95% confidence interval with 1% margin of error, accounting for the bessel's correction. all required samples sizes were below 300 posts, we chose 1,000 posts per time period as a reasonable sample size. figure 3 shows the dynamics for dehumanization by type. we added two notable events to the plot: the vertical blue line signifies the date of publication of the essay "on the historical unity of russians and ukrainians" foot_13 , in which putin publicly questions the legitimacy of ukraine as a state, and the vertical red line shows the start of the 2022 russian invasion of ukraine. we observe that the dehumanization rhetoric, manipulating both dimensions of humanity (low_un_low_nh) is the only type following a stable growth pattern over time. this is the type of dehumanization that is considered indicative of extreme violence risks and includes disgust-driven dehumanization and objectification. this dynamic does not appear to be defined by swift changes in political background or key policy-maker's decisions. the hyper-humanization (high_un_high_nh) is not present due to its absence in the training data. the two types of dehumanization that manipulate one of the two dimensions of dehumanization (high_un_low_nh and low_un_high_nh) demonstrate complex patterns. their frequency starts to increase around 2017, and peaks in 2019 around the time of ukrainian presidential elections 25 , reaching the lowest point in 2021 by the time of the first wave of russia's amassing troops at ukraine's borders 26 . the rapid changes in these dehumanization types suggest that the dissemination of the imagery they supply may be orchestrated, or they are highly sensitive to the shifts in political reality. figure 3 shows that these types start to increase not long before the 2022 invasion and drop at its start, confirming the idea of the preparatory role of dehumanizing rhetoric in sanctioning genocide. notably, the dehumanization signals do not return to the pre-invasion levels with time, suggesting that this phenomenon cannot be localized as only 25 https://www.bbc.com/news/world-europe-48007 487 26 https://www.iiss.org/online-analysis/online -analysis/2021/12/why-is-russia-amassing-troop s-at-its-border-with-ukraine the precursory stage in extreme violence. we observe that different types of dehumanization are evolving at different pace, suggesting that each fulfills a specific role. ## conclusion in this research, we have delved into advanced techniques for dehumanization detection in the backdrop of extreme violence, culminating in the development of the first-ever dataset in the russian language annotated at both sentence and span levels and a spert-based dehumanization detection model showing f 1 = 0.85. leveraging our state-ofthe-art model, our work offers a clear window into the temporal dynamics of dehumanizing rhetoric both before and during the 2022 russian invasion of ukraine, setting a precedent in the field. this system holds potential for integration into systems aimed at predicting and preventing extreme violence and creates a foundation for further research of computational analysis of dehumanization. both best spert model and dataset are available for non-commercial use upon reasonable request and following the intended use; they have not been made publicly accessible to prevent potential malicious use.