# The crime of being poor

Georgina Curto,1 Svetlana Kiritchenko,2 Isar Nejadgholi,2 and Kathleen C.
Fraser2
1University of Notre Dame, Notre Dame, USA
2National Research Council Canada, Ottawa, Canada
<EMAIL_ADDRESS>{Svetlana.Kiritchenko,Isar.Nejadgholi,Kathleen.Fraser}@nrc-
cnrc.gc.ca

###### Abstract

The criminalization of poverty has been widely denounced as a collective bias
against the most vulnerable. NGOs and international organizations claim that
the poor are blamed for their situation, are more often associated with
criminal offenses than the wealthy strata of society and even incur criminal
offenses simply as a result of being poor. While no evidence has been found in
the literature that correlates poverty and overall criminality rates, this
paper offers evidence of a collective belief that associates both concepts.
This brief report measures the societal bias that correlates criminality with
the poor, as compared to the rich, by using Natural Language Processing (NLP)
techniques in Twitter. The paper quantifies the level of crime-poverty bias in
a panel of eight different English-speaking countries. The regional
differences in the association between crime and poverty cannot be justified
based on different levels of inequality or unemployment, which the literature
correlates to property crimes. The variation in the observed rates of crime-
poverty bias for different geographic locations could be influenced by
cultural factors and the tendency to overestimate the equality of
opportunities and social mobility in specific countries. These results have
consequences for policy-making and open a new path of research for poverty
mitigation with the focus not only on the poor but on society as a whole.
Acting on the collective bias against the poor would facilitate the approval
of poverty reduction policies, as well as the restoration of the dignity of
the persons affected.

Artificial Intelligence (AI) provides insights that can trigger innovative
interventions towards the UN Sustainable Development Goals (SDGs) Vinuesa et
al. (2020). The #1 UN SDG is the “end of poverty in all its forms everywhere”,
and there is an urgent call to find alternative paths to fight against
poverty. The trend of global poverty reduction has been decreasing in the last
decades and the Covid-19 pandemic has erased the last four years of poverty
mitigation. Rising inflation and the impact of the war in Ukraine derail the
process of poverty mitigation even further The World Bank (2023). Worldwide,
the World Bank estimates that 685 M people are living below the US$2.15 a day
poverty line The World Bank (2023). Poverty is a worldwide problem that
affects not only the population in developing regions but also a significant
percentage of people living in countries with thriving economies: in the
United States, 11.6% of the population (US 37.9 M people) are in a situation
of poverty and 18.5 M live in extreme poverty United Nations (2018).

The term undeserving poor describes the difficulty for policy makers to
approve and implement poverty reduction policies when the poor are blamed for
their situation, since these policies are not popular Nunn and Biressi (2009).
Therefore, the blamefulness of the poor could have an impact on the actual
poverty levels. Curto et al. Curto et al. (2022) provided evidence of bias
against the poor, or aporophobia Cortina (2017), in Google, Twitter and
Wikipedia word embeddings. This Natural Language Processing approach in
Machine Learning has been widely used to identify biases within AI by
representing words as vectors and measuring meaningful semantic relationships
among them. It also allows us to reflect on societal biases, since the
historical data used in AI has been produced by humans in real world
scenarios.

Caricatured narratives of the rich as being industrious and entrepreneurial
while the poor are seen as wasters and scammers are at the root of this bias,
which has been described as particularly prevalent in the United States United
Nations (2018). The criminalization of poverty refers to a discriminatory
phenomenon where the poor are both associated with criminality and, at the
same time, are being punished for being poor, generating a vicious circle. The
criminal offenses devised for sleeping rough in many cities of the so-called
developed countries are an example of a legalized punishment for being poor,
since they affect the homeless directly.

It must be highlighted that, to date, no evidence has been found that sustains
the high level association between poverty and overall criminality rates. This
correlation is complex to establish due to the multidimensional nature of
poverty, the diversity of crimes (including violent and not violent), the
method to identify them (self-reported and official reports), the potential
police bias in the official crime rates reports, the different indicators of
poverty used to study the potential correlation, the ages of the population in
the sample, and geographic scope Thornberry and Farnworth (1982). However,
recent studies report that factors such as a high poverty headcount ratio,
high income inequality, and unemployment could have an impact on specific
types of crimes, namely related to property Imran et al. (2018); Anser et al.
(2020).

While the criminalization of poverty has been widely denounced as an instance
of bias against the poor, or aporophobia, empirical evidence of this
collective prejudice was missing in the literature. This brief report aims to
fill in this gap. By investigating how poor people are viewed in society
through the analysis of social media texts, namely tweets, we discover that
poor and homeless people are often discussed in association with criminality.
We devise a metric, crime-poverty-bias (CPB), as the difference between the
percentage of utterances mentioning criminality and the poor and the
percentage of utterances mentioning criminality and the rich, and compare CPB
in Twitter for eight English-speaking counties. We present the CPB results per
country together with the factors which could influence the increase of
property criminality rate according to the literature, namely poverty,
unemployment and inequality rates. The purpose of the study is to provide
empirical evidence on the criminalization of poverty and shed some light on
the reasons behind it. We aim to answer the following questions: Can the
differences in the association between poverty and criminality in different
countries be justified based on the respective indicators of poverty,
inequality, unemployment and criminality? Or are these differences due to a
crime-poverty bias? The results have an impact on policy-making since CPB can
hinder the acceptance of poverty mitigation policies by the public opinion.

## Method

We used the Twitter API to collect tweets in English from 25 August 2022 to 23
November 2022, pertaining to two groups: ‘poor’ and ‘rich’. Since tweets can
be up to 280 characters and include several sentences, we split each tweet
into individual sentences. The corpus ‘poor’ comprises tweet sentences that
contain the terms 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. We excluded explicitly offensive terms that tend
to be used in personal insults, such as trailer trash or scrounger. We also
collected tweets related to the group ‘rich’, using 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 included as query terms because of their polysemy
(they can apply to people, but can also be used to describe other things,
e.g., ‘poor results’, ‘rich dessert’). In total, there are over 1.3 million
sentences in the corpus ‘poor’ and over 1.9 million sentences in the corpus
‘rich’.

We were also interested in the geographical locations from which tweets
originated. Unfortunately, only about 2% of tweets included the exact
geographical information. Therefore, in addition to the tweet location we
relied on user location that tweeters voluntarily provide in their Twitter
accounts, which was available for about 60% of tweets. We automatically parsed
user location descriptions to extract country information for the most
frequently mentioned countries. Table LABEL:tab:number-tweets shows the number
of sentences in both corpora per geographical location. In the following
analysis, we focus on eight geographically-diverse English-speaking countries,
for which both corpora contain at least 1,000 sentences each: the United
States of America, the United Kingdom, Canada, India, Nigeria, Australia,
South Africa, and Kenya.

To explore the themes commonly discussed with regard to poor people, we
analyzed the content of sentences within the corpus ‘poor’ using an
unsupervised topic modeling algorithm, BERTopic Grootendorst (2022). Topic
modeling is a Machine Learning technique that aims to group texts
semantically. As a first step, BERTopic converts texts to 384-dimensional
vector representations so that semantically similar texts have similar
representations. This conversion is performed using a sentence transformer, a
large language model trained on over one billion sentences scraped from the
web. Then, the vectors are clustered together using a density-based clustering
technique HDBSCAN (Campello et al., 2013). The clustering algorithm identifies
dense groups of semantically similar texts and leaves texts that do not fit
any clusters as outliers. For computational efficiency, BERTopic was applied
on a random sample of 600,000 sentences from the corpus ‘poor’.

Table 1: The number of tweet sentences in the ‘poor’ and ‘rich’ corpora per geographical location. Location | ‘Poor’ corpus | ‘Rich’ corpus
---|---|---
United States | 326,993 | 460,848
United Kingdom | 80,947 | 135,211
Canada | 32,978 | 43,686
India | 14,029 | 16,296
Nigeria | 10,529 | 26,693
Australia | 9,698 | 14,654
South Africa | 7,729 | 8,600
Kenya | 3,378 | 6,478
Other locations | 337,252 | 461,437
No location information | 539,365 | 748,740
Total | 1,362,898 | 1,922,643
Table 2: Example tweets relating criminality and poor people. The tweet texts
were paraphrased to protect the privacy of the users. Explicit statements
associating the poor with criminality:
---
Put the homeless in jail and make work camps.
More and more homeless people are doing crime.
Homeless people in that area, criminals on the streets!!
Statements opposing the criminalization of poverty, which elicit the
underlying stereotype:
It’s quite bold of you to claim that all homeless people are criminals.
So if you are in poverty you commit violent crimes and murder because you are
disadvantaged?
Law enforcement and prisons are routinely used against poor people not for the
reasons of safety, but to protect the wealthy and privileged.

## Results and Discussion

The topic modeling on sentences related to the group ‘poor’ resulted in 142
extracted topics. Among the top topics in terms of frequency we find expected
discussions such as the lack of affordable housing and (un)fair distribution
of taxes among the socio-economic classes. We also discover themes relating to
drug use, alcohol addiction and mental health issues associated with poverty.
In this paper, we focus on the prominent topic related to criminality, which
includes about 6,000 tweets. This topic is characterized by the presence of
words such as crime, police, cops, criminals, and jail. Some utterances
explicitly associate poverty with crime, while others oppose such positions
and criticize the systemic discrimination of the poor, including over-policing
and disproportionate incarceration of poor and homeless people. However, the
negation of stereotypes through counter-speech (e.g., “not all homeless people
are criminals”) is also a proof that these stereotypes exist in society
Beukeboom and Burgers (2019). Table LABEL:tab:example-tweets shows some
example tweets blaming the poor or denouncing social bias against the group.

Since topic modelling techniques are typically used for qualitative studies, a
quantitative analysis was carried out to complement the results obtained
through BERTopic. We quantified the percentage of sentences from the ‘poor’
corpus (1.3M sentences) that contain the terms related to criminality, per
country (Table LABEL:tab:rates-criminality-country, row 1). The terms related
to criminality include crime, crimes, criminal, criminals, criminalizing,
jail, prison, arrest, arrested. For comparison, we include the percentage of
sentences from the ‘rich’ corpus (1.9M sentences) that contain terms related
to criminality (row 2). We refer to the difference between rows 1 and 2 as the
Crime-Poverty-Bias (CPB), since it measures the rate at which the poor are
related to criminality as compared to the rich.

Table 3: Percentage of sentences that include the terms related to criminality in the ‘poor’ and ‘rich’ corpora and the difference in frequency, which constitutes the Crime-Poverty-Bias (CPB). Percentage of sentences | USA | Canada | South Africa | Kenya | UK | Nigeria | Australia | India
---|---|---|---|---|---|---|---|---
1\. in the ‘poor’ corpus | 3.4 | 2.1 | 2.1 | 1.6 | 1.1 | 0.8 | 1.0 | 0.7
2\. in the ‘rich’ corpus | 1.2 | 0.9 | 1.0 | 0.9 | 0.6 | 0.4 | 0.7 | 0.6
3\. Crime-Poverty-Bias (CPB) | 2.2 | 1.2 | 1.1 | 0.7 | 0.5 | 0.4 | 0.3 | 0.1
Figure 1: For the countries in scope, overview of: the results obtained for
Crime-Poverty-Bias (CPB), poverty headcount at $ 2.5 a day (purchasing power
adjusted prices), overall criminality rate, indicators of inequality (Gini
index and 10% income share) and unemployment rates. Sources: the CPB is
obtained through the authors analysis of a corpus of tweets using the Natural
Language Processing techniques. Poverty headcount ratio, Gini index, and 10%
income share rates (World Bank 2017 or nearest year). Unemployment rate (World
Bank 2021) and overall criminality rate (worldpopulationreview.com).

The results shown in Table LABEL:tab:rates-criminality-country indicate that
CPB is highest in the United States, followed by Canada and South Africa.
Although the literature finds no correlation in general between overall crime
rates and poverty, several factors have been identified that may potentially
lead to an increase in property crime, such as income inequality and
unemployment rate Anser et al. (2020); Imran et al. (2018). If the association
between crime and poverty measured by the CPB reflects reality, we would
expect higher CPB rates in countries rated higher on these measures.
Therefore, to contextualize these outcomes, Figure 1 offers the CPB results
together with each country’s overall criminality rate, poverty headcount ratio
at $2.5 a day (purchasing power adjusted prices), inequality indicators (Gini
Index and 10% income share) and unemployment rate. However, we do not observe
correlation between these indicators and the CPB for the eight countries. The
United States has the highest CPB rates despite having lower poverty,
criminality, inequality and unemployment rates than South Africa. Therefore,
we must look to other causes for the overestimated correlation between poverty
and criminality in the public opinion in the United States. The relatively low
CPB results obtained for other countries such as Kenya, with higher poverty
headcount and similar levels of inequality and unemployment rates to the
United States, would support this hypothesis.

A potential explanation could be found on the narrative shared by the United
States and Canada of being the “land of opportunity” where the rich and the
poor are thought to have equal chance of success and an illusory emphasis on
employment influences the discussion on the public social spending United
Nations (2018). 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 quintile is as low as 7.8%, below European countries such as the
UK, France, Italy, or Sweden Alesina et al. (2018). In fact, intergenerational
mobility has declined substantially over the last 150 years in the United
States Song et al. (2020) and income inequality has been growing since the
1980s The World Bank (2023).

Although the use of Twitter is not representative of the total population
within the countries in scope, the data obtained provides a first approach to
measuring the phenomenon of the criminalization of the poor, which constitutes
an instance of aporophobia. These preliminary results have an impact for
poverty reduction policy making, because when the poor are considered
“undeserving of help” it is more difficult for governments to approve laws to
mitigate poverty. It is also harder for the people in need to overcome their
situation when they are blamed for it and lack support from their community.

This brief report aims to initiate a new path of research for poverty
mitigation, where the focus is not only on the redistribution of wealth but
also on the mitigation of social bias against the poor. While the phenomenon
of bias in terms of gender and race has been extensively analysed, bias
against the poor has not received the attention it deserves both in AI and
social sciences literature, despite the potential impact on the first global
challenge identified by the United Nations.

## References

  * Alesina et al. (2018) Alberto Alesina, Stefanie Stantcheva, and Edoardo Teso. 2018.  Intergenerational Mobility and Preferences for Redistribution.  _American Economic Review_ , 108(2):521–54.
  * Anser et al. (2020) Muhammad Khalid Anser, Zahid Yousaf, Abdelmohsen A. Nassani, Saad M. Alotaibi, Ahmad Kabbani, and Khalid Zaman. 2020.  Dynamic linkages between poverty, inequality, crime, and social expenditures in a panel of 16 countries: two-step GMM estimates.  _Journal of Economic Structures_ , 9(1).
  * Beukeboom and Burgers (2019) Camiel J Beukeboom and Christian Burgers. 2019.  How stereotypes are shared through language: A review and introduction of the social categories and stereotypes communication (SCSC) framework.  _Review of Communication Research_ , 7:1–37.
  * Campello et al. (2013) Ricardo JGB Campello, Davoud Moulavi, and Jörg Sander. 2013.  Density-based clustering based on hierarchical density estimates.  In _Proceedings of the Pacific–Asia Conference on Knowledge Discovery and Data Mining_ , pages 160–172. Springer.
  * Cortina (2017) Adela Cortina. 2017.  _Aporofobia, el rechazo al pobre_.  PAIDOS, Barcelona.
  * Curto et al. (2022) G. Curto, M.F Jojoa Acosta, F. Comim, and B. Garcia-Zapirain. 2022.  Are the poor being discriminated against on the Internet? A machine learning analysis using Word2vec and GloVe embeddings to identify aporophobia.  _AI & Society_.
  * Grootendorst (2022) Maarten Grootendorst. 2022.  BERTopic: Neural topic modeling with a class-based TF-IDF procedure.
  * Imran et al. (2018) Mohammed Imran, Mosharrof Hosen, and Mohammad Ashraful Ferdous Chowdhury. 2018.  Does poverty lead to crime? Evidence from the United States of America.  _International Journal of Social Economics_ , 45(10):1424–1438.
  * Nunn and Biressi (2009) Heather Nunn and Anita Biressi. 2009.  The undeserving poor.  _Soundings_ , 41.
  * Sandel (2020) Michael J. Sandel. 2020.  _The tyranny of merit_.  Penguin Random House.
  * Song et al. (2020) Xi Song, Catherine G. Massey, Karen A. Rolf, Joseph P. Ferrie, Jonathan L. Rothbaum, and Yu Xie. 2020.  Long-term decline in intergenerational mobility in the United States since the 1850s.  _Proceedings of the National Academy of Sciences of the United States of America_ , 117(1):251–258.
  * The World Bank (2023) The World Bank. 2023.  Poverty and Inequality Platform (PIP).
  * Thornberry and Farnworth (1982) Terence P Thornberry and Margaret Farnworth. 1982.  Social Correlates of Criminal Involvement: Further Evidence on the Relationship Between Social Status and Criminal Behavior SOCIAL CORRELATES OF CRIMINAL INVOLVEMENT: FURTHER EVIDENCE ON THE RELATIONSHIP BETWEEN SOCIAL STATUS AND CRIMINAL BEHAVIOR*.  _Source_ , 47(4):505–518.
  * United Nations (2018) United Nations. 2018.  Report of the Special Rapporteur on extreme poverty and human rights on his mission to the United States of America.
  * Vinuesa et al. (2020) Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Daniela Langhans, Max Tegmark, and Francesco Fuso Nerini. 2020.  The role of artificial intelligence in achieving the Sustainable Development Goals.  _Nature Communications 2020 11:1_ , 11(1):1–10.

##  Appendix A Data Collection and Pre-processing

###  A.1 Tweet Collection

We used the Twitter API to collect tweets in English from 25 August 2022 to 23
November 2022, pertaining to two groups: ‘poor’ and ‘rich’. The initial set of
query terms has been assembled from social psychology literature, and expanded
with synonyms and related terms. Then, a one-week sample of tweets collected
using this initial set has been manually examined, and terms that resulted in
very small numbers of retrieved tweets or in many irrelevant tweets were
discarded. The final list of query terms for the group ’poor’ included: 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.
We excluded explicitly offensive terms that tend to be used in personal
insults, such as trailer trash or scrounger. For the group ‘rich’ we used 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 included as
query terms because of their polysemy (they can apply to people, but can also
be used to describe other things, e.g., ‘poor results’, ‘rich dessert’).

###  A.2 Data Pre-processing

We filtered out 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 step helped remove advertisements, spam,
news headlines, and other non-personal communications. Further, tweets
containing query terms from both ‘poor’ and ‘rich’ groups were also excluded.
The remaining tweets were split into individual sentences and only sentences
that included at least one of the query terms were kept. User mentions have
been replaced with ‘@user’ and query terms have been masked with ‘<target>’ to
reduce the bias from the query terms in the analysis. In total, there are over
1.3 million sentences in the corpus ‘poor’ and over 1.9 million sentences in
the corpus ‘rich’.

###  A.3 User Location Identification

To identify the location from which a tweet originated, we used both tweet
location and user location fields that Twitter provides. Only about 2% of the
tweets included the exact geographical information from which the tweet was
sent (the field ‘place’). User location was specified in about 60% of the
tweets. This information was presented as a free-form text, and tweeters were
often very creative in describing their location (e.g., “somewhere on Earth”).
We automatically parsed user location descriptions to extract country
information for the most frequently mentioned countries. In the absence of a
country name, we considered the mentions of U.S. states, Canadian provinces,
and major cities in the U.S., U.K. and Canada, since these were also commonly
used by tweeters. (Major cities from other countries were rarely used without
the country name.)

##  Appendix B Topic Modeling

BERTopic (Grootendorst, 2022) is a flexible state-of-the-art toolkit for
unsupervised, semi-supervised, and supervised topic modeling. The input
documents are first converted to a numerical vector (called embedding) space
using techniques such as sentence transformers. Then the dimensionality of the
embedding space is reduced with techniques like PCA or UMAP since the
clustering methods usually are more effective in low-dimensional spaces. The
core component of BERTopic is a density-based clustering technique HDBSCAN
(Campello et al., 2013), which can produce clusters of different 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 (c-TF-IDF). Topic words
are the words that tend to frequently appear in the topic of interest and less
frequently in the other topics.

We applied BERTopic in the unsupervised mode using the following settings and
parameters. For converting text to numerical representations, we used the
sentence transformers111https://github.com/UKPLab/sentence-transformers method
based on the all-
MiniLM-L6-v2222https://www.sbert.net/docs/pretrained_models.html pre-trained
embedding model. For the vectorizer model, we used the CountVectorizer
method,333https://scikit-
learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
and removed English stopwords and terms that appear 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.