Aporophobia: An Overlooked Type of Toxic Language Targeting the Poor (2023)

Abstract: While many types of hate speech and online toxicity have been the focus of extensive research in NLP, toxic language stigmatizing poor people has been mostly disregarded. Yet, aporophobia, a social bias against the poor, is a common phenomenon online, which can be psychologically damaging as well as hindering poverty reduction policy measures. We demonstrate that aporophobic attitudes are indeed present in social media and argue that the existing NLP datasets and models are inadequate to effectively address this problem. Efforts toward designing specialized resources and novel socio-technical mechanisms for confronting aporophobia are needed.

## introduction online toxicity includes language that is offensive, derogatory, or perpetuates harmful social biases. significant research effort has been devoted to addressing the problem of toxic language targeting several social groups, including women, immigrants, and ethnic minorities @xcite . yet, other groups (e.g., based on age, physical appearance, and socioeconomic status) also regularly experience stigmatization with severe consequences to the groups and to society at large. in this work, we focus on aporophobia-"rejection, aversion, fear and contempt for the poor" @xcite . cortina, the philosopher who coined the term in 1990s, argues that aporophobia is even more common than other forms of discrimination, such as xenophobia and racism. moreover, aporophobia often aggravates intersectional bias (e.g., it is not the same to be a rich woman from an ethnic minority than a poor woman from the same ethnic group) @xcite . in meritocratic societies, the rhetoric of equal opportunities-according to which everyone is provided with the same chances for success-assigns the responsibility for one's welfare to each individual and results in blaming the poor for their fate @xcite . however, this principle does not reflect reality since every person has different abilities and disabilities, backgrounds, and experiences @xcite . in fact, economic indicators unveil a completely different picture: the overwhelming majority of poor people are those born into poverty @xcite . global levels of inequality are increasingly growing (chancel and piketty, 2021), social mobility is as low as 7% both in the united states and in europe @xcite , and the perception of social mobility in the us is higher than the actual opportunities to climb up the ladder, exacerbating even more the blamefulness and criminalization of the poor @xcite . crucially, this bias has an impact on the actual poverty levels: if society considers the poor responsible for their situation and, therefore, "undeserving of help", then measures for poverty mitigation would not be supported, thwarting the efforts towards achieving the first sustainable development goal of the united nations to end poverty @xcite . @xcite states that evolutionary pressure has resulted in innate tendencies toward the search for reciprocity, which in market economies penalizes the poor when they are perceived as benefiting from social programs while offering nothing in return. these tendencies are further aggravated in the current western capitalist context, where wealth is a symbol of success @xcite . what has been described as a "tyranny of merit" (sandel, 2020) manifests unconsciously in our speech and writing as subtle and implicit stereotyping and rejection of the poor. such implicit biased language can be challenging for nlp models that were not specifically trained to recognize this type of abuse @xcite . to date, aporophobia has received little attention in nlp @xcite . in this position paper, we intend to raise awareness of this phenomenon in the community and advocate for the need to study such online behavior, its motivations and expressions, as well as its persistence and spread across online communications, and to design technologies to actively counter aporophobic attitudes. in particular, our goals are as follows: the creation of resources and techniques to effectively confront aporophobia will contribute to both the safety and inclusiveness of online and offline spaces and to the effectiveness of poverty reduction efforts. ## societal impact of aporophobia the current debate on bias and fairness mostly focuses on race and gender-based discrimination. only recently, prejudice and bias against the poor, or aporophobia, has been described as a key distinctive discriminatory phenomenon in the social science literature @xcite . however, international organizations have been denouncing the discrimination and criminalization of the poor for a long time (united nations, 2018). aporophobic attitudes have significant impact at different societal levels. at the micro (personal) level, stigmatization of the poor can inflict significant psychological harm, lead to the internalization of the continuous message of one's inferiority, and contribute to a self-fulfilling prophecy of failure @xcite . at the meso (institutional) level, policies for poverty reduction can be hindered by societal beliefs that the poor are responsible for their own fate and, therefore, undeserving of social assistance @xcite @xcite . finally, at the macro (international) level, aporophobic views are extended to blaming developing countries for their poverty, and prevent reaching fairer deals in international trade and financial markets @xcite . aporophobia affects people across races, genders, and countries. in "voices of the poor," a series of publications that present poor people's own voices in 60 countries @xcite , a common concern has been raised that poor individuals face widespread social disapproval even from people of their own communities, races, genders, and religions. the testimonies describe situations in which "the mere fact of being poor is itself cause for being isolated, left out, looked down upon, alienated, pushed aside, and ignored by those who are better off. this ostracism and voicelessness tie together poor people's experiences across very different contexts" @xcite . the impact of aporophobia is starting to be recognized by national and international organizations. spain was the first country to include aporophobia as a distinct aggravation of hate crimes in the legal framework (spanish criminal code, article 22.4), and aporophobia observatories are being created in several countries, in coordination with the united nations. examining and quantifying aporophobia provides ngos and government officials with new approaches for poverty reduction policy making, acting on public awareness (in addition to redistribution of wealth) and treating poverty as a societal problem, as opposed to a problem of the poor. mitigating aporophobia contributes to the fight against poverty for all ethnic groups and genders @xcite and nlp can play a key role in the identification, tracking and countering of online aporophobia. ## presence of aporophobia in twitter in the first part of the study, we investigated the presence of aporophobia in twitter. for this, we collected and analyzed tweets containing terms related to 'poor people' and contrasted them with tweets related to 'rich people'. then, we performed topic modeling on tweets mentioning the group giftofhome, encampments, encampment, sauda, dera, unsheltered, sacha, nudist, defecating, feces, blankets, shelters, druggies, sidewalk, crackheads, addicts, doorways, tents, shelter, hostels, sidewalks, vagrants, gurmeet, needles, evicting, rahim, sweeps, sleepers, tent, vets, skid, junkies, toothless, outreach, camping, sacramento, sindh, schizophrenic, portland, panhandling, pooping, hobo, evict, motel, fatherless, sodom, hud, isaiah, evicted, housed, addict, motels, veterans, servicemen, fran, denver, camps, hemp, pdx, cashapp, eviction, downtown, accommodation, meth, seattle, subways, depape, streets, junkie, brettfavre, chinatown, unhoused, ebt, shalt, venice, hostel, freeway, newsom, sheltering, francisco, benches, overdoses, surfing, huddled, rv, overdose, reverend, homelessness, euthanasia, addictions, heroin, stray, houseless, belongings, cardboard, rendered, urine, alcoholics, favre, evictions ## unsuitability of existing datasets for studying aporophobia groups based on socio-economic status have been mostly overlooked in nlp research on toxic and biased language. current lexicons designed to identify various types of social biases do not usually include status or socio-economic class categories, and in rare cases when they do, these lexicons are not tailored to identify aporophobia @xcite @xcite . most existing datasets collected for specific targets of abuse (e.g., women, immigrants) do not include poor and low-income subpopulations as a target, with the exception of the dataset for patronizing and condensending language by perez almendros et al. @xcite . to investigate whether these groups appear in datasets collected to study general toxicity (and its various forms), we examined nine frequently used, large, english-language toxicity datasets: dataset data source categories considered total # # of instances 'toxic' of instances mentioning 'poor' all classes 'toxic' civil comments news site comments @xmath0.5 for 'toxicity' 1,999,515 19,140 867 or its subtype wiki toxicity wikipedia comments toxic, severe toxic, obscene, 312,735 168 7 threat, insult, identity attack abusive and hateful twitter hateful, abusive 99,996 51 9 tweets sbic twitter, reddit, offensive 44,875 68 60 stormfront, gab, microaggressions unhealthy comments news site comments unhealthy 44,355 81 3 hate speech and twitter hate speech, offensive 24,783 16 13 offensive tweets cad reddit identity-directed abuse, 23,417 84 17 affiliation-directed abuse, person-directed abuse olid twitter offensive 14,100 16 3 hasoc-2019 twitter, facebook hate speech, offensive, 7,005 19 6 profanity in the civil comments dataset, toxicity was defined as 'general incivility that would likely prompt users to leave the discussion.' not all instances mentioning the group 'poor' and originally labeled as 'toxic' are aporophobic as they can target other entities. we manually examined the civil comments test set for aporophobia, which we defined as 'explicit or implicit expressions of rejection, aversion, or contempt towards poor or homeless people'. first, we looked at the instances originally labeled as 'toxic'. among 11,701 such instances in the test set, only 93 instances mention the group 'poor', and only 21 of them are instances of aporophobia. this clearly demonstrates that existing datasets do not contain a sufficient sample of aporophobia for classifiers to effectively learn the concept. next, we examined instances of the civil comments test set originally labeled as 'non-toxic' by all of the annotators (i.e., with score of zero for all seven toxic categories). we manually annotated a random sample of 300 instances mentioning the group 'poor' and originally labeled 'non-toxic', and found 54 (18%) instances of aporophobia. 5 this indicates that aporophobic views can be expressed very subtly and are deeply rooted in our society so that none of the annotators considered these texts toxic. to further demonstrate the unsuitability of the existing toxic language datasets for modeling aporophobia, we evaluated the performance of three publicly available, high-quality pre-trained robertabased toxicity prediction models on these aporophobic instances: ## discussion our exploratory analysis of tweets revealed a significant presence of aporophobic views expressed or confronted by the users. since only a small percentage of people with low income use twitter (at least in the u.s., the country with the highest number of twitter users), foot_4 the views and opinions about this group come mostly from the out-group. many users felt the need to dispute stereotypical beliefs and discriminatory actions against the poor and the homeless populations, indicating that such views are prevalent in social media and offline. since aporophobia has not received the same attention as other types of discrimination (e.g., based on race or gender), and since it often manifests in subtle and implicit rejection or contempt, aporophobic language may not be perceived as hateful or threatening. nevertheless, it can cause human suffering and jeopardize initiatives to fight poverty, since poverty reduction policies may not be supported when the persons in need are being blamed for their situation @xcite . in a context where the united nations is calling for urgent action to end poverty, nlp techniques allow for a novel view to inform poverty reduction policies by measuring and tracking the various manifestations of aporophobia. such instances can be organized according to the levels of negative action associated with prejudices, documented in cognitive science as avoidance, antilocution, discrimination and physical attack @xcite , at micro (individual), meso (institutional), and macro (national) levels @xcite . furthermore, bias and discrimination have traditionally been studied for individual dimensions (e.g., gen-der, race, etc.) @xcite . yet, different types of biases are often intertwined and aggravate one another @xcite . aporophobia can be incorporated in the intersectional view of bias and discrimination, with complex interrelations with racism, sexism, and xenophobia. but while nlp techniques may be valuable in measuring aporophobic attitudes in written communications-such as news articles, social media, and educational material-current models, lexicons, and datasets are inadequate to effectively address this problem. in addition, expressions of aporophobia cannot simply be banned from public view. alternative strategies for countering aporophobia and mitigating its harms need to be developed. counter-speech and public awareness, as well as institutional and government policies, are some of the tools to reduce prejudice and discrimination against the poor. the nlp community can play a major role in developing such mechanisms in collaboration with social scientists and policy makers. ## conclusion and future work aporophobia is pervasive and entrenched in society, yet so far has been overlooked in nlp research on toxic language. this preliminary study indicates that existing toxic language datasets do not support the development of models for detecting and countering this type of societal bias, and new resources and methods need to be designed and built. however, since toxic and abusive language (including aporophobia) is a relatively rare phenomenon in online communications, random data sampling might be inefficient to collect appropriate amounts of aporophobic statements to characterize the phenomenon in language @xcite . yet, other sampling techniques (e.g., keyword-based, content written by specific users) aiming at increasing the proportion of toxic messages can result in biased data distributions and learnt spurious correlations @xcite . future work should address the problem of collecting data that adequately represents the phenomenon of aporophobia. further, practical annotation guidelines and annotator training programs need to be developed to ensure that annotators have a proper understanding of aporophobia as a concept and can effectively recognize its explicit and implicit manifestations. an aporophobia index @xcite , built and updated by tracking aporophobic views and actions reported or confronted on social media, can help government and non-governmental organizations analyze the trends of this phenomenon and correlate them with economic indicators on poverty and inequality. such an aporophobia index, offering regular updates on aporophobia levels for different geographic locations, would provide valuable insights to tackle poverty as a societal problem, as opposed to a problem of the poor, and define alternative poverty reduction strategies that act on public awareness. research in this field is therefore critical for instrumental reasons: currently 685m people (10% of the total world population) still live in extreme poverty and the covid-19 pandemic could make poverty levels increase by up to 8.3% (united nations, 2022). poverty is a worldwide problem that affects not only developing countries, but also a significant percentage of the population in thriving economies: for example, in the us, 37.9 million people live in poverty @xcite . but fighting aporophobia is also essential because of intrinsic reasons: "recognition of equal dignity and compassion is the key to an ethics of cordial reason and is indispensable to the overcoming of inhumane discrimination" (cortina, 2022).