The Crime of Being Poor: Associations between Crime and Poverty on Social Media in Eight Countries (2024)

Abstract: Negative public perceptions of people living in poverty can hamper policies and programs that aim to help the poor. One prominent example of social bias and discrimination against people in need is the persistent association of poverty with criminality. The phenomenon has two facets: first, the belief that poor people are more likely to engage in crime (e.g., stealing, mugging, violence) and second, the view that certain behaviors directly resulting from poverty (e.g., living outside, panhandling) warrant criminal punishment. In this paper, we use large language models (LLMs) to identify examples of crime–poverty association (CPA) in English social media texts. We analyze the online discourse on CPA across eight geographically-diverse countries, and find evidence that the CPA rates are higher within the sample obtained from the U.S. and Canada, as compared to the other countries such as South Africa, despite the latter having higher poverty, criminality, and inequality indexes. We further uncover and analyze the most common themes in CPA posts and find more negative and biased attitudes toward people living in poverty in posts from the U.S. and Canada. These results could partially be explained by cultural factors related to the tendency to overestimate the equality of opportunities and social mobility in the U.S. and Canada. These findings have consequences for policy-making and open a new path of research for poverty mitigation with the focus not only on the redistribution of wealth but also on the mitigation of bias and discrimination against people in need.

## introduction computational methods provide new insights that can trigger innovative interventions towards the un sustainable development goals (sdgs) @xcite . the "end of poverty in all its forms everywhere" is the #1 un sdg and constitutes an urgent call for action. however, there are still 685m people living in extreme poverty worldwide @xcite , and covid-19 has particularly affected the poorest: the number of people living in extreme poverty rose by 11% in 2020 @xcite . poverty affects not only the population in developing regions but also a significant percentage of those living in thriving economies @xcite : in the united states, 11.6% of the population (37.9m people) are in a situation of poverty @xcite , and within the eu-27, there are 95.3m people (22% of the population) at risk of poverty @xcite . in this context, innovative measures are required to work towards poverty mitigation across the globe. traditional policies based on the redistribution of wealth may not be enough, as evidenced by a deceleration in the poverty reduction rates in the last decades @xcite . interdisciplinary research, incorporating aienabled tools into these efforts, offers perspectives previously unavailable with traditional quantitative and qualitative approaches. prejudice against people with low socioeconomic status can hinder poverty reduction efforts @xcite . when the poor are believed to be responsible for their situation and, therefore, unworthy of help ("undeserving poor"), it is difficult for policy makers to approve and implement poverty reduction policies @xcite . therefore, the blameworthiness of the poor could have an impact on the actual poverty levels. however, aporophobia-a social bias of rejection and contempt for the poor and the associated institutional discrimination of the group @xcite -has only recently become a topic of research, with initial studies providing empirical evidence of this phenomenon @xcite . an important facet of aporophobia is a frequent association of poverty with criminality in society @xcite . it can be seen (1) in public opinion as individuals' beliefs and stereotypes (we refer to this as crime-poverty bias), and (2) in discriminatory actions at an institutional level (we refer to it as institutional criminalization of the poor). in the first instance, homeless people are stereotyped as threatening, violent, not wanting to work, and mentally ill @xcite , and poor people, as a group, are believed to be frequently involved in criminal activities, such as theft and illegal drug dealing. in turn, the criminal offenses devised for sleeping rough in many cities of the so-called developed countries are an example of the institutional criminalization of the poor @xcite . other examples include probation and even incarceration for people who cannot afford to pay minor fines @xcite . the study of the crime-poverty association (cpa) needs to be rooted in cognitive science and the philosophy of discrimination. @xcite explains that human beings interpret information by classifying it into categories based on their previous experience. this process is at the origin of prejudices, which have been described as overgeneralized and, therefore, misleading beliefs that result in systematic and predictable errors in decision making based on available heuristics @xcite . prejudices can lead to social bias reflected in derogatory speech @xcite , and even to discrimination through unfair actions towards members of a group @xcite . in this paper, we use nlp techniques to analyze the prevalence of social media discourse around cpa in eight geographically-diverse countries where english is an official or majority language. despite the fact that social networks are not representative of the whole population, they do act as a partial mirror that allow researchers to measure and track societal biases and discriminatory behaviors that might be hard to detect by traditional quantitative analyses, such as surveys. we employ pre-trained large language models (llms) to classify 500k social media posts in english from the selected countries, identifying statements discussing cpa. we further conduct a topic-modelling analysis to examine the content of the retrieved posts and determine which aspects of cpa are more salient in different regions. finally, we contextualize our findings by comparing them with published indicators of poverty, criminality, and inequality in each of the countries in our study. the preliminary results offered in this article inform about the differences in the social discourse around cpa within the countries in scope and open lines of research towards the mitigation of poverty by acting on biases and discriminatory actions that affect people in need. ## related work we briefly review some of the related social science research on the correlation between crime and poverty, as well as computational work on detecting and responding to social biases. ## data and methods in this project, we aim to analyze and compare the social media discourse on cpa, expressed both in the form of bias and institutional criminalization of the poor, in various regions of the world. we choose twitter as our data source, now called x, foot_0 which provides a rich stream of everyday conversations of ordinary people on a variety of topics. we start by collecting a large set of tweets in english referring to poor people, written by users from eight geographically-diverse countries (section 3.1). next, we automatically retrieve tweet sentences that refer to an association between criminality and poverty. we approach this task as binary text classification with two categories: 'text refers to a crimepoverty association' (or 'cpa') and 'no reference to a crime-poverty association' (or 'not cpa'). this task has not been extensively studied in nlp, and there are no annotated data available. thus, we turn to pre-trained large language models as zeroshot or few-shot text classifiers @xcite . while text classifiers, in general, benefit from finetuning on task-specific labelled datasets, llms have shown to be effective classifiers in scenarios with limited resources @xcite . this is specifically relevant to our study, where we analyze various aspects of cpa in social media discourse across different countries at an aggregate level, and individual labels assigned by llms are not critical. for model selection and validation, we collect and manually label a small test set (described in section 3.2), on which we evaluate the performance of several state-of-the-art llms, listed in section 3.3. after identifying the best performing llm on the test set, we prompt it to automatically categorize the full set of tweet sentences into the 'cpa' and 'not cpa' categories. using this approach, we compute and compare the percentages of tweets about poor people that refer to an association between criminality and poverty, in the eight countries. we further examine the cpa posts to discover and analyze common themes on the issues of criminality and poverty around the world using unsupervised topic modeling (section 3.4). to contextualize our findings, we compare them with the published statistics on various economic and criminality indicators in the studied countries, and speculate on possible reasons for the observed discrepancies in the discussion (section 5). in the following, we describe the dataset and methodology in detail. ## results first, we report the results obtained from the automatic llm-based classification of a large collection of tweets to identify cpa for the eight countries in scope (section 4.1). we analyze these results in the context of economic and criminality indicators for each country. next, we look at the content of cpa posts and examine common themes in the twitter discourse around cpa in different parts of the world (section 4.2). ## discussion our results reveal substantial quantitative and thematic differences in online discourse related to the crime-poverty association in the eight studied countries. the proportion of cpa discussions in twitter in the u.s. is almost twice as high as in the other countries in scope, both in the global north and global south. furthermore, posts from the u.s. and canada more often express bias against poor people as a group, associating them with different kinds of crime. there are many posts portraying homeless people as thieves, muggers, and drug addicts, and calling for systemic measures to remove them from the streets. people receiving welfare support are often described as scammers and not wanting to work. in contrast, tweeters from the other countries are more concerned with unfair laws and regulations that disproportionately affect the poor and aggravate their hardship. a potential explanation for these findings could be found in the narrative shared by the united states and canada of being the "lands of opportunity", where the rich and the poor are thought to have equal chances for success (united nations, 2018). the poor, therefore, would be blamed and even punished for their inability to get out of poverty @xcite . however, the principle of equal opportunity can be considered an oxymoron since every person is exposed to different opportunities in life from the moment of birth (sandel, 2020), and the job market for individuals with low educational qualifications, disability, and with no assistance to find employment is very limited. the indicators of social mobility and inequality support the claim from the united nations that the poor in the united states are overwhelmingly those born into poverty (united nations, 2018): intergenerational social mobility in the united states from the bottom to the top income quantile is as low as 7.8%, below european countries such as the u.k., france, italy, or sweden @xcite . in fact, intergenerational mobility has declined substantially over the last 150 years in the united states @xcite and income inequality has been growing since the 1980s (the world bank, 2023). ## conclusions this paper presents a quantitative and a thematic analyses of the prevalence of online discussions on the association of criminality and poverty in eight geographically-diverse countries. it provides evidence that such discussions more frequently occur within the u.s. and canada, as compared to the other countries such as south africa, despite the latter having higher poverty, criminality, and inequality indexes. moreover, the most prevalent topics in these discussions in the u.s. and canada demonstrate negative attitudes and social bias against people in need, for example, portraying homeless people as criminals, muggers, and drug addicts. in contrast, in the other countries in scope, such as nigeria, india or kenya, the most frequently discussed topics refer to the unfairness of laws that do not provide support or even discriminate against the poor. we speculate that these differences can be partially attributed to the rhetoric of equal opportunities widespread in the u.s. and canada. the insights obtained from this study shed light towards a new path of research for poverty mitigation, where the focus should be not only on the redistribution of wealth but also on the mitigation of social bias and discrimination against the poor.