Corresponding author: Juan Manuel Pérez (e-mail: jmperez at dc.uba.ar).

# Assessing the impact of contextual information in hate speech detection

JUAN MANUEL PÉREZ1  FRANCO LUQUE2  DEMIAN ZAYAT7  MARTÍN KONDRATZKY10  AGUSTÍN
MORO6, 8  PABLO SANTIAGO SERRATI6, 9  JOAQUÍN ZAJAC6, 11  PAULA MIGUEL6, 9
NATALIA DEBANDI12  AGUSTÍN GRAVANO4, 5, 6  and VIVIANA COTIK1, 3  Instituto de
Ciencias de la Computación, CONICET, UBA (e-mail: {jmperez, vcotik} at
dc.uba.ar)  Facultad de Astronomía, Matemática y Física, Universidad Nacional
de Córdoba (email: francolq at famaf.unc.edu.ar)  Departamento de Computación,
Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires
Laboratorio de Inteligencia Artificial, Universidad Torcuato Di Tella (email:
agravano at utdt.edu)  Escuela de Negocios, Universidad Torcuato Di Tella
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)  Facultad
de Derecho, Universidad de Buenos Aires (email: dzayat at derecho.uba.ar)
Universidad Nacional del Centro de la Provincia de Buenos Aires (email:
agustin.moro at azul.der.unicen.edu.ar)  Instituto de Investigaciones Gino
Germani, Facultad de Ciencias Sociales, Universidad de Buenos Aires  Facultad
de Filosofía y Letras, Universidad de Buenos Aires  Escuela Interdisciplinaria
de Altos Estudios Sociales, Universidad de San Martín  Universidad Nacional de
Río Negro (email: nataliadebandi at gmail.com)

###### Abstract

In recent years, hate speech has gained relevance in social networks and other
digital media due to its intensity and its association with violent acts
against members of protected groups. Facing huge amounts of user-generated
contents, a great effort has been made to develop automatic tools to aid the
analysis and moderation of this kind of speech, at least in its most
threatening forms. One of the limitations for current approaches on automatic
hate speech detection is the lack of context. The focus on isolated messages,
without considering any type of conversational context or even the topic being
discussed, severely restricts the available information in orther to determine
whether a post in a social network should be tagged as hateful or not. In this
work, we assess the impact of adding contextual information to the hate speech
detection task. In particular, we study a Twitter subdomain consisting of
replies to posts by digital newspapers and media outlets, which provides a
natural environment for contextualized hate speech detection. We built an
original corpus in ”Rioplatense” Spanish dialect focused on hate speech
associated with the COVID-19 pandemic. A sample of this corpus was manually
annotated using carefully designed guidelines. Our classification experiments
using state-of-the-art transformer-based machine learning techniques show
evidence that adding contextual information improves the performance of hate
speech detection for two proposed tasks: binary and multi-label prediction,
increasing their Macro F1 by 4.2 and 5.5 points, respectively. These results
highlight the importance of the use of contextual information in hate speech
detection. Our code, models, and corpus has been made available for further
research.

###### Index Terms:

NLP, Text Classification, Hate Speech detection with contextual information,
Spanish annotated corpus, COVID-19 Hate Speech

=-15pt

##  I Introduction

Hate speech can be described as speech containing denigration and violence
towards an individual or a group of individuals, based on certain
characteristics protected by international treaties, such as gender, race,
language, and others [1]. In recent years, this type of discourse has taken on
great relevance due to its intensity and its prevalence on social media. The
exposition to this phenomenon has been related to stress and depression in the
victims [2], and also to the setting of a hostile and dehumanizing ground for
immigrants, sexual and religious minorities, as well as other vulnerable
groups [3]. Adding to the psychological effects, one of the most worrying
aspects of hate speech on social media is its relationship with violent acts
against members of these groups, such as the “Unite the Right” attacks at
Charlottesville [4], the Pittsburgh synagogue shooting [5], and the Rohingya
genocide at Myanmar [6, 7], among others. As a result, states and
supranational organizations such as the European Union have enacted
legislation that urges social media companies to moderate and eliminate
discriminatory content, with a particular focus on that which encourages
physical violence [8].

The last two years have seen a dramatic increase in the prevalence of hate
speech amid the COVID-19 pandemic, featuring targets such as Chinese, Asian
and Jews, among other nationalities and minorities, blaming them for the
spread of the virus or the increase in inequalities [9]. The dissemination of
fake news related to conspiracy theories and other types of disinformation
[10, 11] has been linked to an increase in violence against members of these
groups [9].

Great effort has been made in recent years in the research and development of
automatic tools to aid the analysis and moderation of hate speech, at least in
its most threatening forms [12, 13, 14, 15]. From a Natural Language
Processing (NLP) perspective, hate speech detection can be thought of as a
text classification task: given a document generated by a user (i.e., a post
in a social network), predict whether or not it contains hateful content [14].
Additionally, it may be of interest to predict other features, such as whether
the text contains a call to take some possibly violent action, whether it is
directed against an individual or a group, or which characteristics are
attacked [16], for example.

One of the limitations of the current approaches to automatic hate speech
detection is the lack of context. Most studies and resources work with data
without any kind of context - i.e., isolated user messages with no information
about the conversational thread or even the topic being discussed- [17]. This
limits the available information to discern if a comment is hateful or not,
given that an expression can be injurious in certain contexts, but not in
others.

Another limitation is that most resources for hate speech detection are built
in English, restricting the research and applicability to other languages [14,
15]. While there are some datasets in Spanish [16, 18, 19], to the best of our
knowledge, none is related to the COVID-19 pandemic, which shows distinctive
features and targets in comparison to other hate speech events. Besides, none
of the existing datasets comes from the Rioplatense dialectal variety of
Spanish, which has its own particularities and might express hate speech in a
distinct way.

In the present work, we address the issues described above regarding hate
speech detection: 1) we consider finer-grained distinctions that go beyond a
binary detection of hateful vs. non-hateful speech, such as the identification
of attacked characteristics and the detection of calls to action; 2) we study
the impact of adding contextual information to the classification problems,
and 3) we approach the problem in Spanish, a language with relatively few
resources available for this task. We are especially interested in the second
issue, regarding the usefulness of contextual information; this is the main
research question of this work.

For these purposes, we built a dataset based on user responses to posts from
media outlets on Twitter. This subdomain of social networks (i.e., responses
to news posts) is particularly interesting because it provides a natural
context for the discussion (the news post under debate) while also replicating
the interactions of a news forum. We collected a Spanish dataset of news
related to the COVID-19 pandemic and had it annotated by native speakers.
Classification experiments using state-of-the-art techniques based on BETO
[20], a Spanish version of BERT (Bidirectional Encoder Representations from
Transformers) [21], show evidence that adding context improves detection both
in a binary setting (predicting the presence or absence of hate speech) and in
a fine-grained setting (predicting the attacked characteristics and whether
there is a call to action). These results highlight the importance of
contextual information for hate speech detection. Figure 1 provides a
graphical, high-level overview of the work discussed in this paper.

Our contributions are the following:

  1. 1.

We describe the collection, curation and annotation process of a novel corpus
for hate speech detection based on user responses to news posts from media
outlets on Twitter. This dataset is in the Rioplatense dialectal variety of
Spanish and focuses on hate speech associated with the COVID-19 pandemic.

  2. 2.

Through a series of classification experiments using state-of-the-art
techniques, we show evidence that including contextual information improves
the performance of hate speech detection, both in binary and fine-grained
settings.

  3. 3.

We make our code, models and the annotated corpus available for further
research.111Our code and corpus will be publicly available once the paper is
published. If needed before, please write to the corresponding author.

The rest of the paper is organized as follows: Section II reviews previous
work for automatic hate speech detection. Section III states the definition of
hate speech used in this work, along with the targeted groups and the
characteristics of interest. Section IV describes the process performed to
collect and annotate our corpus, which is later used in Section V to conduct
our classification experiments. Section VII discusses the results and Section
VIII draws conclusions and outlines possible future work.

Figure 1: Work overview. The process starts with the collection of data from
Twitter, according to a sampling procedure destined to achieve a balanced
proportion of attacked characteristics. The dataset is later annotated by
native speakers following carefully designed annotation guidelines. The
annotated corpus is used to train and evaluate models for hate speech
detection, both as a binary and a multi-label classification task. Our
experiments reveal that contextualized models outperform non-contextualized
ones.

##  II Previous Work

Hate speech has attracted a lot of attention in recent years, with literature
from the legal and social domains studying its definition and classification
[22], the elements that enable its identification, and its relationship to
freedom of expression and human rights [1, 23]. The automatic detection of
this phenomenon is usually approached as a classification task, and is related
to a family of other tasks such as cyberbullying, offensive language, abusive
language, toxic language, and others. Waseem et al. [24] propose a typology of
these related tasks by asking whether the offensive content is directed to a
specific entity or group, and whether the content is explicit or implicit.

There is a plethora of resources for the automatic detection of hate speech.
Interested readers can refer to Poletto et al. [17] for an extensive review of
datasets for this task. In particular, Spanish corpora are scarce, despite its
being one of the most used languages in social media, and the second language
in the number of native speakers worldwide [25]. To the best of our knowledge,
all available datasets for this language have been published in the context of
shared tasks. Fersini et al. [19] presented a $\sim$4k Twitter dataset for the
Automatic Misogyny Identification (AMI) shared task (IberEval 2018222IberEval
2018: https://sites.google.com/view/ibereval-2018?pli=1). The MEX-A3T task
(IberEval 2018 and IberLEF 2019333IberLEF 2019:
https://sites.google.com/view/iberlef-2019/) included a dataset of $\sim$11k
Mexican Spanish tweets annotated for aggressiveness [26, 27]. Basile et al.
[16] published a $\sim$6.6k tweets dataset annotated for misogyny and
xenophobia, in the context of the HatEval challenge (SemEval 2019 444SemEval
2019: https://alt.qcri.org/semeval2019/).

Due to the COVID-19 pandemic, a spike in the incidence of hate speech has been
documented in social networks [28]. Some works have addressed its distinctive
features, studying hateful dynamics in social networks [29] and also
generating specific resources for the analysis and identification of this kind
of toxic behavior [30]. AnonymousAuthors [31] describe a work-in-progress on
this research of hate speech in Spanish tweets related to newspaper articles
about the COVID-19 pandemic.

Regarding techniques for our specific task, classic machine learning
techniques such as handcrafted features and bags of words over linear
classifiers have been applied [12, 32, 33]. Lately, however, deep learning
techniques such as recurrent neural networks or —more recently— pre-trained
language models have become state-of-the-art [34, 35, 36, 37, 38, 39]. In
spite of the great results achieved by these methods, Arango et al. [40] calls
some of them into question, suggesting that they may be due to possible cases
of overfitting. Plaza-del Arco et al. [41] analyze the currently available
Spanish pre-trained models for hate speech detection tasks.

Since the appearance of GPT (Generative Pre-trained Transformer) [42] and BERT
[21], pre-trained language models based on transformers [43] have become
state-of-the-art for most NLP tasks. These techniques use a transfer-learning
approach, by first pre-training a large language model (thus their name) on a
big corpus, and then fine-tuning it for a specific task (e.g. sentiment
analysis, question answering, or hate speech detection) [42, 44]. This
approach has replaced previous deep learning architectures for most NLP tasks,
which used to be based on recurrent neural networks and word embeddings [45,
46].

Pre-trained models have been built for different languages, and also for
different domains (such as the biomedical [47] and the legal domains [48]) and
text sources (such as Twitter [49] and other social networks). In particular,
Spanish pre-trained models include BETO [20], BERTin [50], RoBERTA-es [51] and
RoBERTuito [52]. Nozza et al. [53] review BERT-based language models for
different tasks and languages.555Note that the names BETO, BERTin, RoBERTA and
RoBERTuito are not acronyms, but alterations of the original name BERT.

Few prior studies incorporate some kind of context to the user comments for
hate speech or toxicity detection. Gao and Huang [54] analyze the impact of
adding context to the task of hate speech detection for a dataset of comments
from the Fox News site. As mentioned by Pavlopoulos et al. [55], this study
has room for improvement: the dataset is rather small, with around 1.6k
comments extracted from only 10 news articles; its annotation process was
mainly performed by just one person; and some of its methodologies are subject
to discussion, such as including the name of the user as a predictive feature.
Mubarak et al. [56] built a dataset of comments taken from the Al Jazeera
website,666https://www.aljazeera.com/ and annotated them together with the
title of the article, but without including the entire thread of replies.

Pavlopoulos et al. [55] analyze the impact of adding context to the toxicity
detection task. They find that, while humans seem to leverage conversational
context to detect toxicity, the trained classification models were not able to
improve their performance significantly by adding context. Following up, Xenos
et al. [57] label each message with its “context sensitiveness”, measured as
the difference between two groups of annotators: those who have seen the
context, and those who have not. With this, they observe that classifiers
improve their performance on comments which are more sensitive to context.

Further, Sheth et al. [58] explore some opportunities for incorporating richer
information sources into the toxicity detection task, such as the interaction
history between users, some kind of social context, and other external
knowledge bases. Wiegand et al. [59] pose some questions and challenges
regarding the detection of implicit toxicity — that is, some subtle forms of
abusive language not expressed as slurs.

Summing up, BERT-based models are state-of-the-art for this type of
classification tasks; there have been various attempts to include context in
distinct ways and with disparate success; there have been relatively few
studies on Spanish data; and hate speech detection has typically been
addressed as a binary task, making no distinction among the attacked
characteristics or calls-to-action. In the present work, we assess the
usefulness of adding context, we work with BERT-based models, on Spanish data,
and address both binary and fine-grained classification tasks.

##  III Definition of Hate Speech

We say that there is hate speech in a comment if it contains statements of an
intense and irrational nature of disapproval and hatred against an individual
or a group of people because of its identification with a group protected by
domestic or international laws [1]. Protected treats or characteristics
include color, race, national or social origin, gender identity, language, and
sexual orientation, among others.

Hate speech can manifest itself explicitly as direct insults, slurs,
celebrations of crimes, incitements to take action against an individual or
group, or even more veiled expressions such as ironic content. Following this
definition, we consider that an insult or aggression is not enough to
constitute hate speech; it is necessary to make an explicit or implicit appeal
to at least one protected characteristic.

For international law, hate speech has an extra element that differentiates it
from offensive behavior: the promotion of violent actions against its targets.
However, the NLP community does not usually require this “call to action” when
identifying hate speech. In the present work, we will adopt this latter view,
and we will explicitly state when we also refer to calls to action.

Several characteristics are taken into account in this work. In addition to
misogyny and racism (the most common treats considered in previous works), we
also consider: homophobia and transphobia; social class hatred (sometimes
known as aporophobia); hatred due to physical appearance (e.g., overweight);
hatred towards people with disabilities; political hate speech; and hate
speech against criminals, prisoners, offenders and other people in conflict
with the law. For this selection, we take into account the definition of
discrimination from international human rights treaties, which refers to
discrimination motivated by race, color, sex, language, religion, political,
or other opinions, national or social origin, property, birth or other status
[60]. These eight characteristics are listed in Table I along with reference
names that will be used throughout the paper.

Short name |  Hate speech against …
---|---
WOMEN |  women
LGBTI |  gay, lesbian, bisexual, transgender, intersexual people
RACISM |  people based on their race, skin color, language, or national identity
CLASS |  people based on their socioeconomic status
POLITICS |  people based on their political affiliation or ideology
APPEARANCE |  fat people, old people, or other aspect-based features
CRIMINAL |  criminals and persons in conflict with law
DISABLED |  people with disability or mental health affections
Table I: Protected characteristics considered in this work. Short names are
used throughout the paper to refer to these broad groups.

##  IV Corpus

Figure 2: Example illustrating the elements in our corpus: a news article
(bottom left), a tweet referring to it (top), and its replies from Twitter
users (bottom right). The user comments are the instances analyzed as
potential hate speech; the original tweet and the article itself are the
context. All texts in this Figure were translated from Spanish to English.

This section describes the collection, curation, and annotation process of the
corpus. Our aim was to construct a dataset of user messages commenting on
specific news articles, in a similar fashion to the reader forums present in
many news outlet websites. Figure 2 offers a schematic illustration of our
dataset, with a tweet from a news outlet about China banning the breeding of
dogs for human consumption, its respective news article, and replies from
users to the original tweet.

###  IV-A Data Collection

Our data collection process was targeted at the official Twitter accounts of a
selected set of Argentinian news outlets: La Nación (@lanacion), Clarín
(@clarincom), Infobae (@infobae), Perfil (@perfilcom), and Crónica (@cronica).
These are the main National newspapers in the country, and attract a vast
volume of interaction on Twitter.

We considered a fixed time period of one year, starting in March 2020. We
collected the replies to each post of the mentioned accounts using the
_Spritzer_ Twitter API, listening to any tweet mentioning one of their
usernames.

For the purpose of this work, we were only interested in the first level of
replies to the original tweet, in order to consider as context only the news
under discussion. If the second or further levels of replies had been
considered, the context would have also contained comments made by other users
(i.e., a conversational thread), which we wanted to avoid. Also, we discarded
tweets from outlets that were not linked to a news article.

To focus our dataset on hate speech related to the COVID-19 pandemic, we only
kept those articles whose body contained one of the following terms:
coronavirus, COVID-19, COVID, Wuhan, _cuarentena_ (quarantine), _normalidad_
(normality), _aislamiento_ (isolation), _padecimiento_ (suffering), _encierro_
(confinement), _fase_ (phase), _infectado_ (infected), _distanciamiento_
(distancing), _fiebre_ (fever) and _síntoma_ (symptom).

Hate speech is not evenly distributed across news articles or topics of
discussion. Previous work has focused on multiple strategies to detect users
or topics around which this phenomenon is prevalent: for example, monitoring
specific targets, hashtags, or offending users [16]. In this case, some form
of sampling strategy is also necessary before the annotation step, since a
random sample of the collected data would bring a very small proportion of
hateful messages.

One of the sampling strategies we considered was to use some keywords to
select interesting articles, taking into account topics that could be a focus
of hate speech. The second strategy considered was to sample articles based on
their comments: news containing comments with common slurs or pejorative
expressions towards our protected groups. That is, we kept only news articles
containing two or more comments that are marked according to a list of
predefined slurs. We selected expressions and slurs that addressed the
protected characteristics considered in our hate speech definition, described
in Section III. The list of slurs and some other technical details are
described in Appendix IX-A.

After some experimentation and subjective evaluation of the articles retrieved
using each strategy, we decided to use the latter one — i.e., to select news
articles based on their user comments — as it seemed to produce the best
results. We emphasize that we sampled the whole article and its comments, and
not just the replies that contained slurs. For each sampled article, 50
comments were chosen at random for annotation, after excluding those with URLs
or images.

Finally, we anonymized tweets by removing user handles and replacing them with
a special @user token, as there are some accounts usually mentioned by hateful
users that could bias the annotation process.

###  IV-B Annotators

Considering that hate speech is usually manifested through jargon and slurs,
and with a strong socio-cultural background, we hired six native speakers of
the Rioplatense dialect of Spanish. Following the lines of Data Statements
[61], we provide in this subsection a characterization of the annotators.

The expected profile of the annotators was of students or graduates of social
sciences, humanities, or related careers, with no experience in artificial
intelligence or data science (to avoid biases in the task). It was also of
interest that they were frequent users of social networks so they could
capture the subtleties of language in that medium.

As part of the recruitment process, they were asked to take a paid test that
consisted in reading the guidelines and annotating ten articles with their
respective comments. After evaluating the results of this test, no applicants
were rejected in this step.

Table II provides disaggregated information about the six annotators hired for
the task. All six had a highly educated profile, and two of them had previous
experience with labeling data. At the time of the study, two of the labelers
were activists in organizations related to some of the vulnerable groups
considered in this work. Four of them identified themselves as members of
groups targeted by hate speech: women and LGBTI (lesbian, gay, bisexual,
transgender and intersex).

Gender | Age | Educ. | Area
---|---|---|---
F | 25-30 | PhD* | Psychology
NB | 30-35 | Undergrad | Arts
F | 30-35 | Undergrad | Anthropology
M | 35-40 | Graduate | Sociology
F | 35-40 | PhD | Psychology
F | 30-35 | Graduate | Communication
Table II: Information about the annotators: gender, age range, education, area
of studies. * indicates ongoing. F stands for female, M for male, NB for non-
binary.

###  IV-C Annotation process

Figure 3: Annotation model for each pair of articles and comments.

To annotate our data, we followed a similar process to the MAMA portion of the
MATTER cycle [62]. First, we defined our model; that is, a practical
representation of what we intended to annotate. Figure 3 depicts the model
used in this work, which follows a hierarchical structure as proposed by
Zampieri et al. [63]. For each comment and its respective context (the tweet
from the news outlet and the full article), a first annotation requires to
mark whether the comment is hateful or not. If it is marked as not hateful, no
further information is required. If it is marked as hateful, two extra
annotations are required:

  * •

An annotation to indicate whether the comment contains a call to action; and

  * •

One or more annotations for each protected characteristic that is attacked by
the message.

Each annotation task comprised a newspaper article along with each of the
selected comments for it. Annotators were given the option of skipping an
article when they considered it irrelevant in terms of hate speech, or when
they did not want to annotate it due to personal reasons (no one actually
skipped an article due to this).

For each article, up to 50 comments were displayed. The annotator had to label
the comments following the hierarchical schema shown in Figure 3. Each article
was first presented to two different annotators with all its comments. Then, a
third annotator only had to annotate those comments marked by at least one of
the previous workers as hateful. While for a majority voting scheme it would
just be necessary to check those with exactly one hateful annotation, an extra
annotation was collected for further experiments.

Before beginning their task, each annotator was required to go through a
training period, which consisted of the test mentioned in Section IV-B and a
second step of annotating 15 articles. This was the only set of articles
labeled by all the annotators. At the end of this stage, they were given
feedback to adjust their criteria, and then proceeded to the actual annotation
task.

###  IV-D Dataset results

Characteristic | Count | Calls to action | $\alpha$
---|---|---|---
RACISM | $2,469$ | 674 | $0.608$
APPEARANCE | $1,803$ | 34 | $0.735$
CRIMINAL | $1,642$ | 722 | $0.618$
POLITICS | $1,428$ | 136 | $0.509$
WOMEN | $1,332$ | 18 | $0.531$
CLASS | $823$ | 135 | $0.404$
LGBTI | $818$ | 11 | $0.555$
DISABLED | $580$ | 4 | $0.596$
Table III: Figures of hateful tweets in our dataset (i.e. annotated by at
least two annotators as hateful), segmented by characteristic with the
corresponding number of tweets calling to action. Inter-annotator agreement is
given for each characteristic, as measured by Krippendorff’s alpha.

The resulting dataset consists of $56,869$ tweets from $1,238$ news articles.
From these tweets, $8,715$ tweets were marked as hateful by two or three
annotators. Table III displays the number of hateful tweets for each of the
considered characteristics. The predominant class of hateful tweets
corresponds to racism, followed by tweets offending by appearance.

Calls to action are mainly directed against criminals, and also driven by
racist motives. Hateful tweets due to class and political reasons have some
tweets in this category as well, and the other characteristics do not account
for much of these violent interactions. Table IV displays some examples of
hateful tweets with their corresponding annotations.

Characteristic |  Context |  Comment
---|---|---
WOMEN |  Around the world: Florencia Peña shows her luxurious new house with bar, dock and pool |  @usuario When you suck the right ones
WOMEN |  Mia Khalifa: acted in porn videos for a few months, became world famous and now fights to erase her past |  @usuario HAHAHAHAHAHAHA KEEP SUCKING….
WOMEN |  Narda Lepes: “They touched my ass a thousand times in restaurant kitchens” |  @user Do you have a nice ass?
LGBTI |  Why Flor de la V did not continue in Mujeres de eltrece, after the departure of Claudia Fontán |  @usuario …because she is not a woman, crystal clear
LGBTI |  Historical: Mara Gómez was enabled and will be the first trans player in Argentine soccer |  @usuario What pair of balls this girl has!!!
LGBTI |  The story of the Colombian trans model kissing the belly of her eight-month pregnant husband |  @usuario A male kissing another male
LGBTI |  This is what actor Elliot Page looks like after declaring himself trans |  @user she has bick777We translated “bija” (a purposely misspelling of “pija”) as “bick”? No. she has pussy? Yes. She is a woman
RACISM |  Coronavirus. Yanzhong Huang: ”It is quite likely that a Covid-21 is already brewing” |  @user Urgent bombs to that damned race
RACISM |  Scientists denounced China’s new maneuver to hide the true figures of the coronavirus |  @user Globally we maintain China because everything comes from there and today we are melted and in an emergency… #ChinaVirus I don’t want to see a #Chinese for a long time!
RACISM |  Impressive operation with tanks for a prosecutor to enter an area controlled by Mapuches |  @usuario Stop it!!! They are not Mapuches, they are criminals!!! Let’s see if someone puts the balls where they have to be put and they shoot them down at once!
CRIMINAL |  Rosario: a group of neighbors beats to death a young man accused of stealing cars |  @user this is great, an example to others
CRIMINAL |  A guy takes the gun from the thief who assaulted him, runs him off and shoots him dead: arrested |  @usuario Great, let’s go for the total extermination of these apes.
CLASS |  Social movements cut off 9 de Julio Av.: they demand a minimum wage of $45,000 |  @user get to work, mfs.
POLITICS |  A new COVID-19 mutation is confirmed, up to 10 times more contagious than the original strain from Wuhan |  @usuario I’M VERY GLAD. I HOPE IT WILL ARRIVE SOON IN ARGENTINA AND DESTROY EVERYTHING. WE COULD FINALLY SEE SOMETHING MORE HARMFUL THAN PERONIST CANCER AND ITS KIRCHNERIST METASTASIS.
Table IV: Some hateful examples of our dataset for each of the considered
characteristics.

From the $8,715$ hateful comments, 77% ($6,777$) contain only one attacked
characteristic, nearly 20% have two or more, and 220 comments have three or
more. Maximum co-occurrence occurs between the characteristics WOMEN and
APPEARANCE, followed by RACISM and CLASS, POLITICS and CLASS, and RACISM and
POLITICS. More information about the co-occurrence of attacked characteristics
can be found in Appendix IX-B.

As suggested by Arango et al. [40], we checked the distribution of users
generating hateful content, so as to avoid having a small number of users
responsible for the majority of offensive interactions. The mean amount of
hateful comments per user is $1.44$, with only 28 users having more than 10
hateful comments.

Inter-annotator agreement was measured via Krippendorff’s alpha [64], using
the implementation included in the `krippendorff` library for
Python.888https://github.com/pln-fing-udelar/fast-krippendorff The agreement
for the hate speech label was $0.579$, which is compatible with other studies
in the area, and expectable considering that we used a rather broad definition
of hate speech [17]. For the _calls-to-action_ label, the agreement was
slighly higher at $0.641$. Individual agreements for each characteristic are
displayed in Table III.

To assign gold labels for each tweet in the dataset, we followed a majority-
vote strategy. A tweet was marked as hateful if at least two annotators
labeled it as such. The CALLS label (calls-to-action) was marked if at least
two annotators selected it, and we marked each characteristic if at least one
annotator selected it. When a tweet was not marked as hateful, no other labels
were assigned.

##  V Classification Experiments

Figure 4: Proposed tasks. The binary task consists in predicting whether a
tweet is hateful or not. The fine-grained task consists in predicting the
attacked characteristics, and whether it calls to action or not.

Now that we have this specially-crafted corpus containing context, we turn our
attention to our original research question: can classifiers leverage context
to improve their performance on the hate speech detection task? For this
purpose, we propose the following classification tasks:

  * •

Binary hate speech detection: Given a tweet, predict whether it is hateful or
not.

  * •

Fine-grained hate speech detection: Given a tweet, predict the attacked
characteristics (if any), and whether it calls to action or not.

In machine learning terms, the binary task can be posed as a binary
classification task, while the fine-grained task is a multi-label
classification task. Figure 4 illustrates the difference between both tasks as
a Venn diagram: in the binary task, we have to predict whether a tweet belongs
to the set of hateful tweets; whereas in the fine-grained one, we have to
predict if a tweet belongs to the set of hateful tweets for each given
characteristic (eight, in our case). The binary task can be seen as a simpler
form of the fine-grained task.

###  V-A Classification algorithms

(a) Binary classifier

(b) Fine-grained classifier

Figure 5: Classification models for the proposed tasks. Three different types
of classifiers are trained according to the type of input: None (no context),
Tweet (context is the tweet to which the comment responds), and Full (context
is the tweet to which the comment responds plus the text of the news article).

For both tasks, we trained algorithms based on state-of-the-art classification
techniques, namely BERT. As explained in Section II, BERT models are based on
Transformer language models pre-trained on large corpora. To adapt them for a
specific task, a fine-tuning process is performed, which consists in removing
the last layer of the language model (usually, a big softmax for the Cloze
task999The Cloze task is a widely used task to evaluate an NLP system’s
language understanding ability, that consists in replacing a missing part of a
text.), replacing it with a proper layer for the downstream task (e.g.
sentiment analysis, question answering), and then adjusting the weights of the
whole model [21, 42].

Since our dataset is in Spanish, we used BETO [20], a monolingual BERT model
for this language. We employed its base version, which consists of 12
Transformer layers with 12 attention heads each, summing up around 100M
parameters.

To assess the importance of having contextual information, we considered three
different types of inputs for the proposed models: the comment without any
context (which we call None), the comment with the tweet to which it responds
as context (Tweet), and the comment with the tweet to which it responds plus
the text of the news article (Full). The special [SEP] token is used to encode
the separation between the context and the analyzed text in the Tweet and Full
inputs (our two context-aware models).

For the binary task, we trained a standard BERT architecture for binary
sequence classification [21], consisting of a sigmoidal output consuming the
last hidden state of the [CLS] token, which acts as a continuous
representation for the whole sentence. For the fine-grained task, we propose a
multi-label output; that is, the simultaneous prediction of the eight
characteristics and the call-to-action label. Figure 5 illustrates both models
for their three different types of inputs.

###  V-B Training

We trained the classifiers following the guidelines of Devlin et al. [21]. We
used Adam [65] as the optimizer, with a weight decay of $0.1$, a peak learning
rate of $5*10^{-5}$ (at the 10% of the optimization steps), and a batch size
of 32. We trained the model for 5 epochs, and selected the best model
according to the F1 score on the dev set. The loss function for the binary
detection task was the binary cross-entropy loss, defined as

$L_{b}(y,\widehat{y})=-y\log(\widehat{y})-(1-y)\log(1-\widehat{y})$

where $y$ is the true label (0 or 1) and $\widehat{y}$ is the predicted
probability of the positive class.

The training process for the fine-grained models was mostly the same, with the
exception of the loss function. As the output of the model is a vector of
probabilities for each output variable (eight characteristics plus call-to-
action), we used a multi-label loss function that considers the probability of
each class independently. Let $d$ be the number of output variables (9 in our
case), $y\in\\{0,1\\}^{d}$ the true label vector, and $\hat{y}\in[0,1]^{d}$
the predicted probabilities. Then, the loss function is defined as:

$L(y,\widehat{y})=\sum\limits_{i=1}^{d}L_{b}(y_{i},\widehat{y}_{i})$

where $L_{b}$ is the binary cross-entropy previously defined.

Sharing the weights between all of the outputs has two benefits: first, it
allows for the creation of a more compact model (otherwise there would be nine
different BERTs adding up to a billion parameters); and second, it enables
sharing common information between the different attacked features. Further
details about the training process can be found in Appendix IX-C.

###  V-C Domain adaptation

Standard training of BERT-based classifiers includes two steps as explained in
Section V-A: the pre-training of the language model and the fine-tuning of the
model to the downstream task [21]. Other transfer-learning approaches in NLP
—such as Universal Language Model Fine-tuning [44]— incorporate an
intermediate step, that adjusts the pre-trained model to the target domain by
continuing the language modeling using the text of the downstream task.
Gururangan et al. [66] showed that continuing the pre-training of BERT-based
models on the target domain improves the performance of the models for several
subdomains of tasks.

In our experimental setup, we adapted BETO using a sample of comments and
articles discarded from the annotation process. As we had three different
types of inputs, we performed three domain adaptations according to the shape
of the input, as shown in Figure 5.

Hyperparameter | Value
---|---
Steps | $10,000$
Batch size | $2,048$
Max Seq. Length | 128, 256 and 512
$\beta_{1}$ | $0.9$
$\beta_{2}$ | $0.98$
$\epsilon$ | $10^{-6}$
Decay | $0.01$
Peak LR | $0.0004$
Warmup ratio | $0.1$
Table V: Hyperparameters used for domain adaptation.

Table V contains the hyperparameters used to adapt the BETO model to our
domain. We used the remaining data of the collection process, consisting of
around $288,000$ articles and $5,000,000$ comments. Three versions of BETO
were fine-tuned, according to each possible input: no context, tweet, and full
context (tweet plus article).

###  V-D Preprocessing

Each tweet was preprocessed using the _pysentimiento_ library [67]: we cut
character repetitions up to three occurrences; laughs were normalized; user
handles were replaced by a special @user token; emojis were converted to a
text representation. Hashtags were stripped, surrounded by a special hashtag
token, and segmented to words if they were camel-cased.

In order to deal with friendlier computational costs, we limited the sequence
lengths to 128, 256, and 512 tokens for the None, Tweet and Full model inputs,
respectively.

###  V-E Evaluation

We split our dataset into training, development and test sets to train and
evaluate our proposed classifiers. To avoid overestimating the performance, we
used a disjoint set of articles for the test set. The training and development
splits comprise $36,420$ and $9,120$ comments respectively, both coming from
990 articles. The test set has $11,343$ comments from 248 articles.

Standard metrics were used for both tasks: precision, recall, F1-score and
Macro F1 score for the binary classification task. For the fine-grained
classification task, we measured F1 for each attacked characteristic, as well
as macro-averaged metrics.

##  VI Results

|  None |  Tweet |  Full
---|---|---|---
Precision |  $71.8\pm 1.6$ |  $\mathbf{74.8\pm 1.9}$ |  $72.8\pm 2.4$
Recall |  $60.2\pm 1.4$ |  $\mathbf{65.3\pm 1.4}$ |  $64.1\pm 2.3$
F1 |  $65.5\pm 0.4$ |  $\mathbf{69.7\pm 0.3}$ |  $68.1\pm 0.6$
Macro F1 |  $79.8\pm 0.2$ |  $\mathbf{82.2\pm 0.2}$ |  $81.3\pm 0.3$
Table VI: Results of classification experiments for the binary detection task.
Each model is a BETO with three possible inputs: the comment alone without
context (None), the comment and the news outlet’s tweet (Tweet), and the
comment plus the news outlet’s tweet plus the article body (Full). Results are
expressed as the mean of ten runs of the experiment along with its standard
deviation.

Table VI displays the results of the binary classification task, measured in
accuracy, precision, recall, F1, and Macro F1. Results are expressed as the
mean of each metric, along with its standard deviation, over ten independent
runs of the experiments. We present the results only for the domain-adapted
BETO classifier; full results can be found in Appendix IX-C. We can observe
that the model consuming the simple context (Tweet) obtains the best results,
with an improvement against the context-unaware (None) model of $4.2$ F1
points on average. The model with the complete context gets worse results than
the model with the simple context, although it improves the general
performance against the context-unaware version.

| Context
---|---
|  None |  Tweet |  Full
CALLS |  $65.1\pm 1.9$ |  $\mathbf{68.5\pm 0.9}$ |  $68.0\pm 1.5$
POLITICS |  $61.1\pm 0.8$ |  $62.5\pm 1.3$ |  $\mathbf{64.8\pm 1.4}$
APPEARANCE |  $74.2\pm 1.0$ |  $\mathbf{76.6\pm 0.9}$ |  $75.8\pm 0.9$
DISABLED |  $58.2\pm 1.3$ |  $\mathbf{60.9\pm 1.8}$ |  $57.8\pm 1.7$
WOMEN |  $38.9\pm 1.5$ |  $\mathbf{42.1\pm 1.7}$ |  $\mathbf{42.1\pm 2.2}$
RACISM |  $65.3\pm 1.0$ |  $\mathbf{72.0\pm 0.4}$ |  $71.1\pm 1.0$
CLASS |  $43.3\pm 1.3$ |  $\mathbf{51.1\pm 2.0}$ |  $47.6\pm 2.7$
LGBTI |  $36.6\pm 1.9$ |  $\mathbf{48.2\pm 1.9}$ |  $44.5\pm 2.1$
CRIMINAL |  $52.9\pm 1.1$ |  $\mathbf{69.9\pm 1.9}$ |  $66.8\pm 1.7$
Macro F1 |  $55.1\pm 0.5$ |  $\mathbf{61.3\pm 0.7}$ |  $59.8\pm 0.6$
Macro Precision |  $63.0\pm 1.8$ |  $\mathbf{70.2\pm 0.9}$ |  $67.8\pm 1.4$
Macro Recall |  $49.9\pm 1.2$ |  $\mathbf{55.1\pm 1.1}$ |  $54.1\pm 1.3$
Table VII: Results of classification experiments for the _fine-grained_ task,
measured as F1 score for each of the characteristics and macro-averaged
metrics. Each model is a BETO with 3 possible inputs: the analyzed comment
alone (None), the comment plus the tweet from the news outlet (Tweet), and
comment plus the news outlet’s tweet plus the article body (Full). Results are
expressed as the mean of ten runs of the experiment along with its standard
deviation.

Table VII shows the results of the classification experiments for the fine-
grained task, measured by F1 score for each of the features and macro-averaged
metrics. As expected, the performance boost of including context is more
evident in this task, with a difference of approximately 6 points between the
context-unaware and context-aware models ($55.1$ vs. $61.3$ Macro F1).
Regarding the two types of context, again the simple version obtains better
performance in practically all the characteristics, with the only exception of
POLITICS.

The characteristics that benefit the most from adding context are CRIMINAL
($+17$ F1 points), LGBTI ($+12$), CLASS ($+8$), and RACISM (almost $+7$); on
the other hand, APPEARANCE and POLITICS benefit the least. It is worth noting
that, even with the help of added context, some characteristics are very
difficult for our classifiers and show a relatively low performance: WOMEN,
LGBTI and CLASS.

###  VI-A Error Analysis

To have a better understanding of the benefits of adding context and also its
limitations, we performed an error analysis between the context-unaware and
context-aware models. To do this, we manually checked the output of ten
classifiers and looked for their most common errors. Table VIII shows a
selection of test instances where context helps to correctly classify
comments, and also some examples where both versions are failing to flag them
as hateful. We can observe that context helps to disambiguate some of the
messages, which are not clearly understood without the additional information.

A remarkable case is that of LGBTI. The mention of any topic-related word in
the headline (such as transgender, gay or lesbian) gives some hint to the
classifiers about the nature of the message. Nonetheless, due to the
complexity of the offenses to transgender individuals (addressing them by
their opposite gender, or slurs about their genitals, for instance) models
usually fail in flagging these messages as hateful.

|  |  Context |  Comment
---|---|---|---
FN without context, TP with context | WOMEN |  Ofelia Fernández supported the Government in the controversy over the prisoners and pointed to the Justice that “hates women” |  motherfuck*r ,, hopefully you will soon receive a visit from one of those worms. They will fit you. Willing to support him. Government? Fat creeping larva. Debrained
| WOMEN |  Did More Rial find love in a personal trainer? |  You have to be hungry to eat that bolivian piglet
| LGBTI |  What Elliot Page looks like after declaring himself transgender |  hope she gets psychiatric help
| LGBTI |  Mara Gómez fulfills her dream: she will be the first transgender footballer in the Argentine professional tournament |  Mara “the club” Gómez
| LGBTI |  Mara Gómez fulfills her dream: she will be the first transgender footballer in the Argentine professional tournament |  go break some legs boy
FP without context, TN with context | LGBTI |  A man got into his car at the door of the Chinese Embassy and claimed that he had explosives |  He is not a man. He’s a jerk
| LGBTI |  Coronavirus in Argentina: 70% of cases are in men |  The corona is female
| LGBTI |  The ruling party calls for a ”federal caravan” in support of the Government and the tax on large fortunes |  Gross
| CLASS |  Paul McCartney: ”The Chinese need to be cleaner and less medieval” |  it had to be said at last
| CLASS |  Main teaching union rejected the return to presential classes |  shitty bums!
Not detected by any classifier | WOMEN |  Why Women-Led Countries Appear To Have Responded Better To The Coronavirus Crisis |  because they wash, iron and sweep?
| WOMEN |  British girl went to Peru for 10 days and stayed for love: she lives with no water and among insects |  she left everything coz of the wood of that Peruvian ahaha that nigger must have a generous dick
| WOMEN |  Did More Rial find love in a personal trainer? |  gotta be well trained to lift that hyppo
| CLASS |  The Government will spend $75B to develop 300 slums throughout the country |  without education behind this is nothing, they will remain the same old misfits but now with Netflix.
| LGBTI |  She told that she was a lesbian, her father confessed that he was gay and now his mother fell in love with a woman: this is how he was inspired for his second film |  The film is called the failure of a normal family
| LGBTI |  “Why don’t we see trans doctors?”: The claim of a prestigious cardiologist for America to be more inclusive |  because sick people cannot heal sick people
| LGBTI |  A trans woman is killed in Rosario after a burst of 20 shots |  Why did she not pull out her shotgun and apply self-defense?!
Table VIII: Error analysis between non-contextualized and contextualized
classifiers. Context and comments are shown. The first group of rows (_FN
—false negatives— without context, TP —true positives— with context_)
represent tweets that were incorrectly labeled as non-hateful by non-
contextualized classifiers, but contextualized classifiers correctly marked as
hateful. The second group consists of tweets that were incorrectly labeled as
hateful by non-contextualized classifiers, but contextualized classifiers
correctly marked them as non-hateful (FP stands for false positives and TN for
true negatives). The last group contains messages that are hateful but were
not detected by any classifier, neither non-contextualized nor contextualized.

##  VII Discussion

For the proposed tasks, we can observe that context seems to give a moderate
improvement in the binary setting, and a more considerable gain in the fine-
grained setting. This result might appear to contradict recent work that found
no improvement by means of contextualization in toxicity detection [55].
However, it must be noted that hate speech is one of the most complex forms of
toxic behavior; thus, hate speech detection might benefit differently from
having additional information. Also, while Pavlopoulos et al. [55]’s context
was extracted from the entire conversation preceding the target message, our
context was taken from the news outlet’s tweet and the article itself under
discussion. Further, Xenos et al. [57] recently found that toxicity detection
algorithms can take advantage of this additional information by restricting
the analysis to a subset of context-sensitive comments.

Something interesting this dataset provides is a characterization of hate
speech. Since we have the attacked characteristics for each hateful tweet, we
could assess the influence of context for each protected characteristic.
Contextual information seems to have more impact on some characteristics than
others (e.g., when the attack is against LGBTI people). Moreover, we can
observe that the dataset has complex and compositional examples of
discriminatory language for some specific characteristics.

The constructed dataset has both short and long contexts. In our experiments,
we have observed no substantial improvement in model performance by using the
long context; that is, the full article. This might coincide with a familiar
behavior observed in humans —that many people comment after reading nothing
but the headline. (However, it might be argued that humans have access to a
richer context and information beyond the headline.)

The experiments performed in this work have a few limitations. First, human
annotators had access to the full contexts when doing their task. To better
assess the impact of context in hate speech detection, context-unaware models
should be trained on comments labeled by humans without access to any
additional information. Second, a practical limitation is that context is not
always available for any given text. Even if we were able to find one, it
might not always consist of a news article — it may also be a conversational
thread, or even audiovisual content, for example. Lastly, the labeled comments
are replies to tweets published by media outlets, which limits the possible
forms of our instances. Therefore, further study is needed to understand how
other forms of messages and contexts impact the detection of hate speech.

##  VIII Conclusions

In this work, we have assessed the impact of adding context to the automatic
detection of hate speech. To do this, we built a dataset consisting of user
replies to Twitter posts published by main news outlets in Argentina, and
annotated it using carefully designed guidelines. We conducted a series of
classification experiments using transformer-based techniques, and found clear
evidence that certain contextual information leads to an improved performance:
our models showed a 4 to 5 point increase in Macro F1 after adding context.

Although in our experiments the smallest context (the news article tweet) was
the one that obtained the best results, a future line of work could explore
ways to include other sources of information. For instance, adding real-world
knowledge about the targets of hate speech could be useful. This information
might be even available in the news article itself, or other sources such as a
knowledge graph.

From the error analysis, it can be seen that some categories of hate speech
are elusive for state-of-the-art detection algorithms. One of these cases is
the abusive messages against the LGBTI community, which contain semantically
complex messages, with ironic content and metaphors that are difficult to
interpret for classifiers based on state-of-the-art language models. Despite
these limitations, the detection of hate speech against the LGBTI community
was among the most benefited by the addition of context. Future work should
explore the reasons behind the difficulties for the state-of-the-art models to
detect it, and also explore ways to improve the detection of this type of hate
speech.

We may conclude that hate speech detection clearly benefits from the use of
contextual information. The evidence from our experiments —preliminary for
now, and with the limitations noted in the discussion— indicates that state-
of-the-art models can use this information to improve the detection of hate
speech on social networks. We hope that this work will encourage the use of
contextual information in the detection of hate speech and other opinion
mining tasks, and that it will be a starting point for future research in this
area.

## Availability of Data and Material

We make our corpus available at the huggingface hub
101010https://huggingface.co/datasets/piubamas/contextualized_hate_speech. For
the sake of reproducibility and also for further research, we will release the
anonymized annotations (as suggested by Basile [68]) in addition to the
aggregated dataset. The annotation guidelines will be publicly available upon
publication of this paper.

## Acknowledgments

The authors would like to thank the annotators who worked to ensure the
accuracy and quality of the data used in this study. Their dedication and hard
work were instrumental in the success of this project. Thanks to A. Silva, G.
Clerici, G. Damill, D. Valado, F. de Sanctis, L. Prats.

We would also like to thank Dr. Eugenia Mitchelstein, who provided valuable
insights and suggestions that helped shape the direction of this research.

This research work was supported by grants for interdisciplinary research
projects evaluated and funded by the Universidad de Buenos Aires
(PIUBAMAS-2020-3 and PIUBA-2022-04-02). We would also like to thank CONICET
and Universidad Torcuato Di Tella for their support.

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|  Juan Manuel Pérez received his MSc (2016) and Ph.D. degree (2022) in
computer science at Universidad de Buenos Aires. He is currently a
postdoctoral researcher at the Instituto de Ciencias de la Computación,
Facultad de Ciencias Exactas, UBA/CONICET, working on contextualized detection
of hate speech and opinion mining tasks in social networks using NLP
techniques.
---|---
|  Franco M. Luque has a degree and a Ph.D. in computer science (FAMAF,
National University of Córdoba, Argentina). He works as Adjunct Professor at
the Faculty of Mathematics, Astronomy, Physics and Computing (FAMAF), National
University of Córdoba, and as an Assistant Researcher at the CONICET. His main
area of research is NLP, and he is also involved in research projects on
various topics such as analysis of hate speech in social networks, multimodal
learning for visual dialogue and information extraction in medical reports.
---|---
|  Demian Zayat is a Constitutional Law Professor. He holds a law degree
(2000) from Universidad de Buenos Aires and a master’s degree from Stanford
University (JSM, 2009). His main area of research is Human Rights and
discrimination, combining legal and data-driven research.
---|---
|  Martín Kondratzky has a degree in linguistics (Universidad de Buenos
Aires). He is currently working as an NLP engineer in the fields of
information retrieval and question answering while finishing his master’s
degree in statistics.
---|---
|  Agustín Moro holds a Ph.D. in Sociology and is a Professor of Scientific
Research Methodology at Universidad Nacional del Centro. He also works as a
Consultant in the field of evidence-based policy implementation processes.
---|---
|  Pablo Santiago Serrati is a Ph.D. candidate in social sciences at the
Faculty of Social Sciences (UBA). He works on the use of data analysis
techniques and statistical programming in the fields of Urban Studies and
Social Science.
---|---
|  Joaquín Zajac holds a degree in sociology from Universidad de Buenos Aires,
a master’s in Social Anthropology and a Ph.D. in social sciences. Post-doc
fellow (CONICET) at Universidad de San Martin. He specializes in violence,
human rights and security issues, combining qualitative and quantitative data
analysis for his research work.
---|---
|  Paula Miguel holds a degree and a Ph.D. in social sciences. She is a
Professor of Sociology and Researcher at the Universidad de Buenos Aires,
specializing in cultural analysis, qualitative analysis and, more recently,
data science and NLP techniques.
---|---
|  Natalia Debandi holds a degree in computer science (UBA) and a Ph.D. in
Social Sciences from the University of Buenos Aires and the University Paris
IV Sorbonne. She specializes in the design of human rights indicators and data
analysis for academic and applied human rights research.
---|---
|  Agustín Gravano is Associate Professor at Universidad Torcuato Di Tella and
Independent Researcher at CONICET, in Argentina. His research focuses on
building computational models of coordination in spoken dialogue, for later
improving the naturalness of spoken dialogue systems. He received his Ph.D. in
computer science from Columbia University in 2009.
---|---
|  Viviana Cotik is an Assistant Professor at Universidad de Buenos Aires and
a Research Assistant at CONICET, in Argentina. Her research area is NLP and
Data Science and focuses on information extraction from biomedical texts. She
received her Ph.D. in Computer Science in 2018.
---|---

##  IX Supplemental material

###  IX-A Data selection

Expression | Description or translation
---|---
viejo puto | old fag
marica | fag
sodomita | sodomite
degenerados | degenerate
trabuco, trava | slur for transgender woman
travesti | transgender woman
bija | misspelling of dick
feministas | feminists
feminazis | offensive term against feminists
aborteras | abortion activists
gorda | fat woman
uno menos | one less (celebratory expression for a killing)
urraca | magpie (offensive slur against a woman)
prostituta | prostitute
putita | little bitch
reventada | prostitute
peruano, peruca | peruvian
paraguayo | paraguayan
trolo | fag
bala | bullet (as in “shoot them”); also fag
bolita | slur for bolivian
negro(s) (de) | nigger
judío, sionista | jew, zionist
matarlos | (have to) kill them
chinos | chinese
una bomba | a bomb
vayan a laburar/trabajar | go to work
villeros | shanty dwellers
Table IX: Seed expressions used to select articles based on possibly hateful
comments.

Table IX lists the seed expressions used to mark potentially hateful comments.
This list was constructed manually, checking for some common expressions in
the data. We used MongoDB’s text index to retrieve any comments containing at
least one of them.

Some of these expressions were used literally (with quotation marks) and some
were allowed inflections provided by the search engine. For some of them, we
excluded other words: for instance, when querying “negra” (_female nigger_) we
removed “plata — guita” (_money_) as there were many hits for such queries.
For others, we added prepositions to the query (such as “negro de”) because
using just “negro” had a lot of non-hateful hits.

It is important to stress that this method was only used for selecting news
articles for the subsequent annotation step, and comments were randomly
sampled among the replies to the selected articles.

###  IX-B Additional information of the dataset

Newspaper | #Art | #Comm
---|---|---
@infobae | 590 | $26,834$
@clarincom | 370 | $17,501$
@LANACION | 222 | $10,378$
@cronica | 42 | $1,562$
@perfilcom | 14 | $594$
Total | $1,238$ | $56,869$
Table X: Number of articles and comments in the dataset per news outlet

Table X displays the number of articles and comments in the final dataset. We
can observe that most articles and comments come from @infobae, followed by
@clarincom and @LANACION.

(a) Co-occurrence of attacked characteristics within the same comment

(b) Co-occurrence of attacked characteristics within the same article

Figure 6: Co-occurrence matrices for attacked characteristics in hateful
messages. Figure 6(a) shows co-occurrence within the same comment, and Figure
6(b) shows co-occurrence across comments of the same article. Brighter
indicates more co-occurrence

From the $8,715$ hateful comments present in the dataset, 77% of them
($6,777$) contain only one attacked characteristic, nearly 20% have exactly
two, and 220 comments have three or more. Figure 6 illustrates the co-
occurrence matrix between the different characteristics for comments having
more than one attacked characteristic. We can observe that the maximum co-
occurrence occurs between the characteristics WOMEN and APPEARANCE, followed
by RACISM and CLASS, POLITICS and CLASS, and RACISM and POLITICS.

Another way of analyzing co-occurrence is by grouping the different
characteristics of their comments by articles, to observe how the same context
can invoke different types of discrimination. Figure 6(b) illustrates the
interactions between the different characteristics per article. Greater
dispersion is observed in the co-occurrences than in Figure 6(a), showing some
additional interactions such as between RACISM and POLITICS and —perhaps
unexpectedly— between APPEARANCE and POLITICS.

###  IX-C Classification experiments

| Context
---|---
| None | Tweet | Full
Metric |  BETO |  BETOFT |  BETO |  BETOFT |  BETO |  BETOFT
Accuracy |  $88.9\pm 0.3$ |  $89.9\pm 0.2$ |  $90.2\pm 0.2$ |  $91.0\pm 0.2$ |  $90.4\pm 0.2$ |  $90.5\pm 0.3$
Precision |  $67.8\pm 2.0$ |  $71.8\pm 1.6$ |  $73.1\pm 1.1$ |  $74.8\pm 1.9$ |  $73.9\pm 1.6$ |  $72.8\pm 2.4$
Recall |  $56.8\pm 1.7$ |  $60.2\pm 1.4$ |  $60.1\pm 1.0$ |  $65.3\pm 1.4$ |  $61.1\pm 1.6$ |  $64.1\pm 2.3$
F1 |  $61.8\pm 0.5$ |  $65.5\pm 0.4$ |  $66.0\pm 0.6$ |  $69.7\pm 0.3$ |  $66.9\pm 0.5$ |  $68.1\pm 0.6$
Macro F1 |  $77.6\pm 0.3$ |  $79.8\pm 0.2$ |  $80.1\pm 0.3$ |  $82.2\pm 0.2$ |  $80.6\pm 0.2$ |  $81.3\pm 0.3$
Table XI: Results of the classifiers for the binary task, expressed as the
mean and standard deviation of ten independent runs of the experiments. Three
different types of inputs are considered: no context, comment + tweet of the
news outlet, and full context. FT means that the pre-trained language model
was fine-tuned, and $\neg$FT means it was not fine-tuned.

Table XI and Table XII display the full results for the binary and fine-
grained tasks. We used two pre-trained language models as our base models:
BETO, without any fine-tuning on the data (marked as $\neg$FT), and a BETO
fine-tuned with the remaining data of the collection process, as described in
Section V-A. The results show that, in all cases, the fine-tuning process
improves the performance of the classifiers.

To train our classification models, we used the _HuggingFace_ library [69] and
the _PyTorch_ framework [70]. We used a _NVIDIA GeFORCE GTX 1080 Ti_ to fine-
tune the models. To perform the domain-adaptation of the language models, we
used a _TPU v2-8_ in a _Google Colab Pro_ instance, taking 10 hours at its
maximum sequence length.

| Context
---|---
| None | Tweet | Full
Metric |  $\neg$FT |  FT |  $\neg$FT |  FT |  $\neg$ FT |  FT
CALLS |  $64.6\pm 1.0$ |  $65.1\pm 1.9$ |  $63.8\pm 0.9$ |  $68.5\pm 0.9$ |  $65.3\pm 1.3$ |  $68.0\pm 1.5$
WOMEN |  $37.3\pm 1.3$ |  $38.9\pm 1.5$ |  $41.1\pm 0.9$ |  $42.1\pm 1.7$ |  $38.1\pm 1.7$ |  $42.1\pm 2.2$
LGBTI |  $35.1\pm 1.8$ |  $36.6\pm 1.9$ |  $45.1\pm 2.1$ |  $48.2\pm 1.9$ |  $42.7\pm 2.4$ |  $44.5\pm 2.1$
RACISM |  $63.5\pm 1.4$ |  $65.3\pm 1.0$ |  $68.8\pm 1.2$ |  $72.0\pm 0.4$ |  $69.1\pm 0.9$ |  $71.1\pm 1.0$
CLASS |  $40.1\pm 1.6$ |  $43.3\pm 1.3$ |  $49.1\pm 2.2$ |  $51.1\pm 2.0$ |  $45.1\pm 1.9$ |  $47.6\pm 2.7$
POLITICS |  $55.5\pm 1.8$ |  $61.1\pm 0.8$ |  $57.9\pm 1.4$ |  $62.5\pm 1.3$ |  $59.1\pm 1.3$ |  $64.8\pm 1.4$
DISABLED |  $55.1\pm 1.6$ |  $58.2\pm 1.3$ |  $58.5\pm 1.6$ |  $60.9\pm 1.8$ |  $55.7\pm 2.3$ |  $57.8\pm 1.7$
APPEARANCE |  $72.6\pm 1.0$ |  $74.2\pm 1.0$ |  $74.1\pm 1.2$ |  $76.6\pm 0.9$ |  $75.5\pm 0.9$ |  $75.8\pm 0.9$
CRIMINAL |  $51.3\pm 1.4$ |  $52.9\pm 1.1$ |  $65.0\pm 1.2$ |  $69.9\pm 1.9$ |  $65.4\pm 2.3$ |  $66.8\pm 1.7$
Macro Precision |  $55.8\pm 1.0$ |  $63.0\pm 1.8$ |  $64.2\pm 1.6$ |  $70.2\pm 0.9$ |  $67.7\pm 1.4$ |  $67.8\pm 1.4$
Macro Recall |  $50.6\pm 0.6$ |  $49.9\pm 1.2$ |  $54.0\pm 0.8$ |  $55.1\pm 1.1$ |  $50.4\pm 0.9$ |  $54.1\pm 1.3$
Macro F1 |  $52.8\pm 0.5$ |  $55.1\pm 0.5$ |  $58.2\pm 0.5$ |  $61.3\pm 0.7$ |  $57.3\pm 0.7$ |  $59.8\pm 0.6$
Table XII: Results of the classifiers for the fine-grained task, expressed as
the mean and standard deviation of ten independent runs of the experiments.
Three different types of inputs are considered: no context, comment + tweet of
the news outlet, and full context. FT means that the pre-trained language
model was fine-tuned, and $\neg$FT means it was not fine-tuned.