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

Abstract:
Content Warning: This paper presents textual examples that may be offensive or upsetting. 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.

text: The 7th Workshop on Online Abuse and Harms (WOAH), pages 113–125 July 13, 2023 c©2023 Association for Computational Linguistics
Content Warning: This paper presents textual examples that may be offensive or upsetting.
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.

heading: 1 Introduction
text: 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 (Fortuna and Nunes, 2018; Kiritchenko et al., 2021). 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” (Cortina, 2022). 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) (Hoffmann, 2019; Hellgren and Gabrielli, 2021).
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 (Mounk, 2017; Sandel, 2020). However, this principle does not reflect reality since every person has different abilities and disabilities, backgrounds, and experiences (Fishkin, 2014). In fact, economic indicators unveil a completely different picture: the overwhelming majority of poor people are those born into poverty (United Nations, 2018). 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 (Chetty et al., 2014; OECD, 2018), 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 (Alesina et al., 2018).
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 (Arneson, 1997; Applebaum, 2001).
Cortina (2022) 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 (Fraser and Honneth, 2003). 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
113
models that were not specifically trained to recognize this type of abuse (Wiegand et al., 2019; Nejadgholi et al., 2022).
To date, aporophobia has received little attention in NLP (Curto et al., 2022). 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:
• Characterize aporophobia as a distinct discriminatory phenomenon with significant societal impact, based on social science literature;
• Demonstrate that aporophobic attitudes are common in society and prominent in social media;
• Show that existing toxic language datasets are ill-suited for training automatic systems to address this type of prejudice due to (1) the lack of adequate sample of aporophobic instances, and (2) the failure of human annotators to recognize implicit aporophobic statements and attitudes as part of a general definition of harmful language.
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.

heading: 2 Societal Impact of Aporophobia
text: 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 (Cortina, 2022). 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 (Habermas, 1990; Honneth, 1996). 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 (Applebaum, 2001; Everatt, 2008; Nunn and Biressi, 2009). 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 (Reis et al., 2005; Yapa, 2002).
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 (Narayan and Petesch, 2002), 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” (Narayan and Petesch, 2002).
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 (Everatt, 2008) and NLP can play a key role in the identification, tracking and countering of online aporophobia.

heading: 3 Presence of Aporophobia in Twitter
text: 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,
‘poor’ and examined topics related to aporophobia. In the following, we discuss these steps in detail.

heading: 3.1 Tweet Collection
text: We polled the Twitter API to collect English tweets for a period of three months, from 25 August 2022 to 23 November 2022, using query terms related to poor and homeless people. The initial set of query terms was assembled from the social science literature on the “undeserving poor” (Everatt, 2008; Narayan and Petesch, 2002; Applebaum, 2001) and aporophobia (Cortina, 2022; Comim et al., 2020). The set was expanded with synonyms and related terms. Then, a one-week sample of tweets collected using this set of terms was manually examined. Terms that resulted in very small numbers of retrieved tweets or in many irrelevant tweets were discarded. We also excluded explicitly offensive and derogatory terms, such as trailer trash, scrounger, or redneck, which tend to be used in personal insults. The final list of query terms for the group ’poor’ was: the poor (used as a noun as opposed to an adjective as in ‘the poor performance’), poor people, poor ppl, poor folks, poor families, homeless, on welfare, welfare recipients, low-income, underprivileged, disadvantaged, lower class.
As a contrasting set, we also collected tweets related to the group ’rich’ using the following query terms: the rich (used as a noun), rich people, rich ppl, rich kids, rich men, rich folks, rich guys, rich elites, rich families, wealthy, well-off, upper-class, upper class, millionaires, billionaires, elite class, privileged, executives. The single words poor and rich were not part of the search due to their polysemy (e.g., ‘poor results’, ‘rich dessert’). Using the selected terms, we were able to collect a large amount of relevant tweets without costly manual filtering.
We excluded re-tweets, tweets with URLs to
external websites, tweets with more than five hashtags, and tweets from user accounts that have the word bot in their user or screen names. This filtering step helped to remove advertisements, spam, news headlines, and so on. Further, tweets containing query terms from both ‘poor’ and ‘rich’ groups were also excluded. In the remaining tweets, user mentions were replaced with ‘@user’ and query terms were masked with ‘<target>’ to reduce the bias from the query terms in the analysis. In total, there were 1.3M tweets for the group ‘poor’ and 1.8M tweets for the group ‘rich’.

heading: 3.2 Word Analysis
text: Words which are often used in tweets describing ‘poor people’, but rarely used in tweets describing ‘rich people’, are expected to be the most representative words associated with the group ‘poor’. Thus, we calculated the score of association with the group ‘poor’ using the following formula:
s (w) = PMI (w ,Cpoor )− PMI (w ,Crich) (1) where PMI stands for Pointwise Mutual Information and was calculated as follows:
PMI (w ,C ) = log2 freq (w ,C ) ∗N(T ) freq (w ,T ) ∗N(C) (2)
where freq (w, C) is the number of times the word w occurs in corpus C, freq (w, T) is the number of times the word w occurs in corpus T = Cpoor ∪ Crich, N(C) is the total number of words in corpus C, and N(T ) is the total number of words in corpus T . Stopwords and low-frequency (< 300 occurrences in Cpoor) words were disregarded.
Table 1 shows 100 words with the highest association to the group ‘poor’. Note that these words include many terms related to alcohol and drug abuse (e.g., addicts, meth, alcoholics) and mental disorders (schizophrenic). Many tweeters also complained about unsanitary environments often
surrounding homeless encampments and city sidewalks occupied by homeless people. Further, euthanasia appears in this list since many users were concerned (or some users supported) that it could become a solution to end the suffering of the poor.

heading: 3.3 Topic Modeling
text: Next, we analyzed the thematic content of tweets mentioning the group ‘poor’. For this, we employed an unsupervised topic modeling toolkit, BERTopic (Grootendorst, 2022). The core component of BERTopic is a density-based clustering technique HDBSCAN (Campello et al., 2013), which can produce clusters of arbitrary shapes and leave documents that do not fit any clusters as outliers. This suited our case well as we wanted to discover the most commonly discussed topics in tweets mentioning poor people. The discovered topics are then represented with topic words, which are identified using class-based TF-IDF (cTF-IDF). The ‘topic words’ are the words that tend to appear frequently in the topic of interest, and less frequently in the other topics.
To reduce computational costs, we applied topic modeling on a random subsample of 600K sentences from Cpoor. For converting text to numerical representations, we used the sentence transformers method based on the all-MiniLM-L6-v2 pre-trained embedding model.1 For the vectorizer model, we used the CountVectorizer method,2
1https://www.sbert.net/docs/pretrained_models .html
2https://scikit-learn.org/stable/modules/gene rated/sklearn.feature_extraction.text.CountVecto
and removed English stopwords and terms that appeared in less than 5% of the sentences (min_df = 0.05). For the HDBSCAN clustering algorithm, we specified the minimum size of the clusters as min_cluster_size = 500. For all the other parameters, the default settings of the BERTopic package were used.
There were 142 topics extracted. We found a number of expected topics discussing the issues of homeless encampments in city parks and streets, the lack of affordable housing, the need to provide shelter and free meals to the homeless, (un)fair distribution of taxes among the socio-economic classes, Christian dogmas of helping the poor, criticism or support of government policies, and various related local issues. We also observed a number of topics with more derogatory and vilifying attitudes, portraying the poor, and especially the homeless, as drug addicts, drunkards, criminals, mentally disabled, and expendable, or expressing general feelings of fear and rejection of the group. Table 2 shows example tweets for some of these topics. Several topics tie the issues of poverty and homelessness with specific communities, such as Black people, immigrants and refugees, and veterans. Not all tweets on these topics express aporophobic views. Some report aporophobic situations, and many actually oppose such attitudes and criticize individuals and policies that hurt the poor. However, even when stereotypes are negated (e.g., ‘not all homeless people are drug addicts’), the syntactic form preserves the stereotype-consistent
rizer.html
information (here: ‘all homeless people are drug addicts’), confirming the stereotypic association (Beukeboom and Burgers, 2019). That is, the existence of such counter-speech is indirect evidence that such stereotypes and biases exist.

heading: 4 Unsuitability of Existing Datasets for Studying Aporophobia
text: 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 (Nicolas et al., 2021; Kozlowski et al., 2019; Smith et al., 2022).
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. (2020). 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:
1. Civil Comments Dataset (Borkan et al., 2019): a dataset of public comments from Englishlanguage news sites annotated through crowdsourcing for toxicity and six toxicity subtypes (severe toxicity, obscene, threat, insult, identity attack, and sexual explicit) with realvalued scores that represent the fraction of annotators who assigned the category to the comment. We considered a comment ‘toxic’ if it had score > 0.5 for at least one of the seven toxic categories.
2. Wiki Toxicity (Wulczyn et al., 2017): a dataset of comments from Wikipedia talk page edits annotated through crowd-sourcing for six categories of toxicity: toxic, severe toxic, obscene, threat, insult, and identity attack. We considered a comment ‘toxic’ if it was labeled with any of the six categories of toxicity.
3. Abusive and Hateful tweet corpus by Founta et al. (2018): a large corpus of tweets annotated through crowd-sourcing for hateful, abusive, spam, and normal language. We considered a tweet ‘toxic’ if it was labeled as ‘hateful’ or ‘abusive’.
4. Social Bias Inference Corpus (SBIC) (Sap et al., 2020): a collection of tweets, Reddit posts, posts from the hate communities Stormfront and Gab, and posts from a corpus of microaggressions annotated through crowd-sourcing for offensiveness, intent to offend, sexual content, target group, whether the speaker is part of the target group, and the implied statement. We considered a text ‘toxic’ if it was labeled as ‘offensive’.
5. Unhealthy Comments Corpus (Price et al., 2020): a dataset of public comments from the Globe and Mail news website annotated through crowd-sourcing for ‘healthy’ vs. ‘unhealthy’ and for six potentially ‘unhealthy’ categories: (1) hostile; (2) antagonistic, insulting, provocative or trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic; and (6) an unfair generalisation. We considered a comment ‘toxic’ if it was labeled as ‘unhealthy’.
6. Hate Speech and Offensive Language tweet corpus by Davidson et al. (2017): a dataset of tweets annotated through crowd-sourcing for three categories: (1) hate speech, (2) offensive language, and (3) neither hate speech nor offensive. We considered a tweet ‘toxic’ if it was labeled as either ‘hate speech’ or ‘offensive language’.
7. Contextual Abuse Dataset (CAD) (Vidgen et al., 2021): a dataset of posts and comments from Reddit annotated for identitydirected abuse, affiliation-directed abuse, person-directed abuse, counter-speech, nonhateful slurs, and neutral. The annotations were done by two annotators, and the disagreements were resolved through an expert-driven group-adjudication process. We considered a text ‘toxic’ if it was labeled as ‘identitydirected abuse’, ‘affiliation-directed abuse’, or ‘person-directed abuse’.
8. Offensive Language Identification Dataset (OLID) (Zampieri et al., 2019a): a dataset of tweets annotated through crowd-sourcing for offensiveness (offensive or not), type of offense (targeted insult or untargeted), and target of offense (individual, group, or other). We considered a tweet ‘toxic’ if it was labeled as ‘offensive’.
9. HASOC-2019 (Mandl et al., 2019): a dataset of tweets and Facebook posts annotated by its creators for hate speech, offensive content, and profanity, and whether the offense is targeted or untargeted. We considered a tweet ‘toxic’ if it was labeled as ‘hate speech’, ‘offensive’, or ‘profanity’.
(More details on the datasets are provided in Appendix A.) We did not consider datasets annotated exclusively for hate speech since socio-economic status is not considered an attribute that defines a protected group in legal terms and, therefore, none of the existing hate speech datasets include the group ‘poor’ as a target in their definitions of hate speech.
We used the same query terms for the group ‘poor’ that we used in Sec. 3. Table 3 shows the number of instances containing these terms in the selected datasets.3 While the sizes of the datasets vary from a few thousand to two million, most contain only a few dozen instances mentioning the group ‘poor’, and only a handful of these instances are labeled as toxic/offensive.4 These datasets tend to be collected using query terms that frequently occur in toxic content targeting groups based on gen-
3Since SBIC has the targeted group explicitly labeled, we searched for words poor, poverty, and homeless in the targeted group description.
4Most instances mentioning the group ‘poor’ in SBIC are labeled ‘toxic’ as the majority of these instances are jokes collected from intentionally offensive subReddits.
der, ethnicity, or religion, and thus may not capture toxic content about poor people. The only noticeable exception is the Civil Comments dataset that includes over 19K instances mentioning the group ‘poor’. This is due to its size and to the fact that it comprises all online news comments collected through the Civil Comments platform, without any filtering. Notice, however, that the overwhelming majority of the messages mentioning the group ‘poor’ (over 95%) are labeled as non-toxic. This further demonstrates that topics related to poor people are frequently discussed online, but this group is rarely a target of NLP studies on toxicity.
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:
1. Detoxify6 (Hanu and Unitary team, 2020): an open-source multi-class model fine-tuned on the Civil Comments dataset;
2. Wiki+Civil7: a binary toxicity model finetuned on the combination of the Wiki Toxicity and Civil Comments datasets;
3. TweetEval8 (Barbieri et al., 2020): a RoBERTa-based model trained on 58M English tweets and fine-tuned on the OLID dataset.
Table 4 shows the recall these models achieve on the aporophobic instances originally labeled as either ‘toxic’ or ‘non-toxic’, i.e., the percentage of the aporophobic instances for which the models predicted the toxicity score > 0.5. For comparison, we also show precision and recall for the Toxic class these models achieve on the full Civil Comments test set. Observe that while the models demonstrate good overall performance on the test set and moderate to high recall on aporophobic instances originally labeled as ‘toxic’, they all fail to recognize aporophobia in more implicit instances that also proved challenging for human annotators. Overall, we conclude that the existing toxic language datasets are ill-suited for training effective
5Similarly, we found instances originally labeled ‘nontoxic’ that contain aporophobic views in the CAD (6 out of 65) and Unhealthy Comments (11 out of 78 instances).
6https://huggingface.co/unitary/unbiased-tox ic-roberta
7https://huggingface.co/SkolkovoInstitute/rob erta_toxicity_classifier
8https://huggingface.co/cardiffnlp/twitter-r oberta-base-offensive
models for aporophobia detection, and new datasets specifically targeting this phenomenon are urgently needed.

heading: 5 Discussion
text: 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),9 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 (Arneson, 1997).
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 (Allport, 1954), at micro (individual), meso (institutional), and macro (national) levels (Comim et al., 2020). Furthermore, bias and discrimination have traditionally been studied for individual dimensions (e.g., gen-
9https://www.pewresearch.org/internet/2021/04 /07/social-media-use-in-2021/
der, race, etc.) (Hoffmann, 2019). Yet, different types of biases are often intertwined and aggravate one another (Lalor et al., 2022). 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.

heading: 6 Conclusion and Future Work
text: 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 (Schmidt and Wiegand, 2017; Founta et al., 2018). 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 (Wiegand et al., 2019). 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 (Comim et al., 2020),
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 (Creamer et al., 2022). 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).

heading: 7 Limitations
text: In this exploratory study, we focused on Englishlanguage resources. Further, we examined only one social media platform, Twitter. As any other platform, Twitter has a biased demographic representation of users in terms of language, location, ethnicity, gender, age, socio-economic status, and other characteristics. In particular, Twitter is predominantly used in the United States.10 As a result, user attitudes examined in this study primarily represent Western views and may differ significantly from views common in other regions of the world. Future studies on aporophobia need to include other languages and world regions and consider cultural differences while measuring and mitigating this type of social bias.
When searching for aporophobia-related texts, we excluded derogatory terms and slurs associated with the group ‘poor’ as such explicit forms of online abuse tend to be easier to detect by human
10https://www.statista.com/statistics/242606/n umber-of-active-twitter-users-in-selected-count ries/
annotators and NLP models. Nevertheless, when designing tools for measuring and mitigating aporophobia such explicit expressions need to be taken into account. Furthermore, there is a wide variety of linguistic expressions referring to poor and homeless people, and sometimes this target group is not even mentioned at all, but could be inferred from the context (e.g., contexts referring to hunger, food stamps and/or other benefits, ghettos, etc.). To effectively confront aporophobia, NLP resources (lexicons, datasets, classification models) need to have a wide coverage of explicit and implicit linguistic expressions of the phenomenon.
Finally, we targeted only textual data. However, many social media posts combine text with other types of data, such as images and videos. Recent techniques for modeling multi-modal data can be employed to ensure a better coverage of various types of social media posts.
Ethics Statement
Confronting aporophobia, as an application similar to addressing other types of abusive and toxic language, poses a number of risks and ethical issues, including tension between freedom of speech and respect for equality and dignity, biased data sampling and data annotation, dual use, and many others, discussed in previous works by Hovy and Spruit (2016); Vidgen et al. (2019); Leins et al. (2020); Vidgen and Derczynski (2020); Cortiz and Zubiaga (2020); Kiritchenko et al. (2021); Salminen et al. (2021). Future research on this topic should comply with trustworthy AI principles of transparency, justice and fairness, non-maleficence, responsibility, and privacy (Jobin et al., 2019). Special attention should be paid to involving all legitimate stakeholders in the identification and definition of actions to counteract aporophobia, including the affected communities, non-governmental organizations (NGOs) and government officials working on poverty mitigation. In particular, the views and needs of the communities from both the Global North and the Global South should be included.

heading: A Existing Toxicity Datasets
text: We used nine large, English-language, toxicity datasets:
• Civil Comments Dataset11 (Borkan et al., 2019): The dataset includes public comments from about 50 English-language news sites across the world posted in 2015-2017 via the Civil Comments platform. The comments were annotated for toxicity and six toxicity subtypes (severe toxicity, obscene, threat, insult, identity attack, and sexual explicit) through crowdsourcing. Each toxicity and toxicity subtype score is a real value which represents the fraction of annotators who believed the category applied to the given comment. The dataset is released under CC0, as is the underlying comment text, and was used in the Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge.
• Wiki Toxicity12 (Wulczyn et al., 2017): The dataset consists of comments from Wikipedia’s talk page edits. The comments were annotated through crowd-sourcing for six categories of toxicity: toxic, severe toxic, obscene, threat, insult, and identity attack. The dataset is released under CC0, with the underlying comment text being governed by Wikipedia’s CC-SA-3.0. It was used in the Jigsaw Toxic Comment Classification Kaggle challenge.
• Abusive and Hateful Tweet Corpus by Founta et al. (2018)13: This is a large corpus of tweets annotated through crowd-sourcing for hateful, abusive, spam, and normal language. The tweets were selected using a boosted random sampling technique where a random sample was complemented with tweets that showed strong negative polarity and that contained at least one offensive word. This boosting procedure helped improve the coverage of the minority (non-normal) classes since hateful and abusive tweets tend to appear quite rarely in the Twitter stream. We used an updated version of the dataset with 100K annotated tweets. The dataset is available by requesting access from the authors.
11https://www.kaggle.com/competitions/jigsaw-u nintended-bias-in-toxicity-classification/data
12https://www.kaggle.com/competitions/jigsaw-t oxic-comment-classification-challenge/data
13https://github.com/ENCASEH2020/hatespeech-t witter
• Social Bias Inference Corpus (SBIC)14 (Sap et al., 2020): The dataset contains textual instances collected from various sources: posts from three intentionally offensive subReddits (r/darkJokes, r/meanJokes, r/offensiveJokes), posts from two English subreddits that were banned for inciting violence against women (r/Incels and r/MensRights), posts from known English hate communities Stormfront and Gab, posts from a corpus of microaggressions (Breitfeller et al., 2019), and a sample of tweets from three existing English Twitter datasets created by Founta et al. (2018); Waseem and Hovy (2016); Davidson et al. (2017). Each instance was annotated via crowd-sourcing for offensiveness, intent to offend, sexual content, target group, whether the speaker is part of the target group, and the implied statement. The dataset is publicly available. We used version 2.
• Unhealthy Comments Corpus15 (Price et al., 2020): The dataset includes public comments from the Globe and Mail (a large Canadian newspaper) news website randomly sampled from the SFU Opinion and Comment Corpus dataset (Kolhatkar et al., 2020). Only comments with 250 characters or less were included in the sample. The comments were annotated through crowdsourcing for the binary category ‘healthy’ vs. ‘unhealthy’ and for the presence of six potentially ‘unhealthy’ categories: (1) hostile; (2) antagonistic, insulting, provocative or trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic; and (6) an unfair generalisation. All labels are binary and include confidence scores. The labels and confidence scores were obtained as aggregated answers of multiple annotators taking into account the annotators’ ‘trustworthiness’ scores. The dataset is released under CC BY-NCSA 4.0.
• Hate Speech and Offensive Language Tweet Corpus by Davidson et al. (2017)16: The dataset consists of tweets collected using hateful words and phrases compiled by Hatebase.org. The tweets were annotated through crowdsourcing for three categories: (1) hate speech, (2) offensive language but not hate speech, and (3) 14https://maartensap.com/social-bias-frames/ 15https://github.com/conversationai/unhealth y-conversations 16https://github.com/t-davidson/hate-speech-a nd-offensive-language
neither hate speech, nor offensive. The dataset is publicly available.
• Contextual Abuse Dataset (CAD)17 (Vidgen et al., 2021): The dataset contains a stratified sample of posts and comments from 16 subReddits, which were identified as likely to contain higher-than-average levels of abuse. The messages were collected over 6 months from 1st February 2019 to 31st July 2019. All posts and comments were manually annotated within the context of conversational threads for six primary categories: identity-directed abuse, affiliationdirected abuse, person-directed abuse, counterspeech, non-hateful slurs, and neutral. Each instance was assigned to one or more of the six categories. The annotations were done by two annotators, and the disagreements were resolved through an expert-driven group-adjudication process. The dataset is released under CC Attribution 4.0 International. We used version 1.1.
• Offensive Language Identification Dataset (OLID)18 (Zampieri et al., 2019a): The dataset consists of tweets collected using query terms and constructions that are often included in offensive messages, such as ‘you are’, ‘she is’, ‘gun control’, ‘MAGA’, etc. The tweets were annotated via crowd-sourcing for offensiveness (offensive or not), type of offense (targeted insult or untargeted), and target of offense (individual, group, or other). The dataset is publicly available. It was used in the shared task SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (Zampieri et al., 2019b), and is part of the evaluation benchmark TweetEval (Barbieri et al., 2020).
• Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC2019)19 (Mandl et al., 2019): The dataset consists of Twitter and Facebook posts collected using hashtags and keywords that contained offensive content. The posts were manually annotated by the creators of the dataset for hate speech, offensive content, and profanity, and whether the offense is targeted or untargeted. The dataset is publicly available and was used in the first edi-
17https://zenodo.org/record/4881008#.Y6dTinbMI uU
18https://github.com/cardiffnlp/tweeteval 19https://hasocfire.github.io/hasoc/2019/datas
et.html
tion of the HASOC track at FIRE 2019. We used only the English portion of the dataset.

heading: Future Work
text: Future studies on aporophobia need to include other languages and world regions and consider cultural differences while measuring and mitigating this type of social bias. When searching for aporophobia-related texts, we excluded derogatory terms and slurs associated with the group ‘poor’ as such explicit forms of online abuse tend to be easier to detect by human
10https://www. statista. com/statistics/242606/n umber-of-active-twitter-users-in-selected-count ries/
annotators and NLP models. Nevertheless, when designing tools for measuring and mitigating aporophobia such explicit expressions need to be taken into account. Furthermore, there is a wide variety of linguistic expressions referring to poor and homeless people, and sometimes this target group is not even mentioned at all, but could be inferred from the context (e. g. , contexts referring to hunger, food stamps and/or other benefits, ghettos, etc. ). To effectively confront aporophobia, NLP resources (lexicons, datasets, classification models) need to have a wide coverage of explicit and implicit linguistic expressions of the phenomenon. Finally, we targeted only textual data. However, many social media posts combine text with other types of data, such as images and videos. Recent techniques for modeling multi-modal data can be employed to ensure a better coverage of various types of social media posts