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The role of sarcasm in hate speech. A multilingual perspective

Abstract

The importance of the detection of aggressiveness in social media is due to real effects of violence provoked by negative behavior online. For this reason, hate speech online is a real problem in modern society and the necessity of control of user-generated contents has become one of the priorities for governments, social media platforms and Internet companies. Current methodologies are far from solving this problem. Indeed, several aggressive comments are also disguised as sarcastic. In this perspective, this research proposal wants to investigate the role played by creative linguistic devices, especially sarcasm, in hate speech in multilingual context.
The role of sarcasm in hate speech.
A multilingual perspective
La funci´on del sarcasmo en los discursos de odio.
Una perspectiva multiling¨ue
Simona Frenda1 2
1PRHLT Research Center, Universitat Polit`ecnica de Val`encia, Spain
2Dipartimento di Informatica, Universit`a degli Studi di Torino, Italy
simona.frenda@unito.it
Abstract: The importance of the detection of aggressiveness in social media is due
to real effects of violence provoked by negative behavior online. For this reason, hate
speech online is a real problem in modern society and the necessity of control of user-
generated contents has become one of the priorities for governments, social media
platforms and Internet companies. Current methodologies are far from solving this
problem. Indeed, several aggressive comments are also disguised as sarcastic. In this
perspective, this research proposal wants to investigate the role played by creative
linguistic devices, especially sarcasm, in hate speech in multilingual context.
Keywords: Hate speech, social media, aggressiveness, misogyny, sarcasm
Resumen: La importancia del reconocimiento de la agresividad en las redes so-
ciales es debido al hecho que esas conductas negativas se traducen en violencias en
la vida real tambi´en. Por esa raz´on los discursos de odio online son un problema
real en nuestra sociedad y la necesidad del control de los contenidos generados por
usuarios se ha convertido en una de las prioridades de gobiernos, de las redes so-
ciales y de empresas de Internet. Las metodolog´ıas corrientes est´an lejos de resolver
este problema. De hecho gran parte de los comentarios agresivos son disfrazados
como sarcasticos. En esta perspectiva, esta propuesta de investigaci´on propone de
estudiar la funci´on de las figuras ret´oricas, con particular atenci´on al sarcasmo, en
los discursos de odio en un contexto multiling¨ue.
Palabras clave: Discursos de odio, redes sociales, agressividad, misoginia, sarcasmo
1 Introduction
The web facilitates the large resonance of
hate speech, inciting racism, misogyny or
xenophobia also in the real world. Actually,
it is common that misbehaviours online are
traduced in physical attacks, such as rapes
or bulling. For instance, Fulper et al. (2014)
demonstrated the existence of a correlation
between the number of rapes and the amount
of misogynistic tweets per state in USA, sug-
gesting the fact that social media can be used
as a social sensor of violence.
In addition, the persistence and diffu-
sion of misogynistic or offensive content can
hurt and distress psychologically the victims,
causing sometime their suicide, such as the
case of the teenager Amanda Todd in 20121.
In order to contrast the origin of these hate
1https://www.theguardian.
com/commentisfree/2012/oct/26/
events and to monitor the uncontrolled flow
of users texts, several initiatives have been
taken in the last years. An example is the
campaign No Hate Speech Movement2of the
Council of Europe for human rights online.
The growing interest of NLP (Natural
Language Processing) research community is
demonstrated by the proposal of national
and international workshops (such as ALW
20183) or campaigns of evaluation fostering
the research in this issue in various languages,
such as EvalIta 20184, IberEval 20185and Se-
mEval 20196. These initiatives allow to share
amanda-todd-suicide-social-media-sexualisation
2https://www.coe.int/en/web/
no-hate-campaign
3https://sites.google.com/view/alw2018
4http://www.evalita.it/2018
5https://sites.google.com/view/
ibereval-2018
6http://alt.qcri.org/semeval2019/index.
Lloret, E.; Saquete, E.; Mart´ınez-Barco, P.; Moreno, I. (eds.) Proceedings of the Doctoral Symposium of the XXXIV
International Conference of the Spanish Society for Natural Language Processing (SEPLN 2018), p. 1317 Sevilla, Spain,
September 19th 2018. Copyright c
2018 by the paper’s authors. Copying permitted for private and academic purposes.
information and results exploring the differ-
ent topics regarding the hate speech online.
As well as, the organizers of these compe-
titions provide resources such as annotated
datasets that are very costly to obtain.
The fact that the majority of data are col-
lected from Twitter or Facebook supports the
analysis of the computer-mediated communi-
cation. As well as, the context of short text
incites the creativity of authors who use fig-
urative devices to express their opinion. One
of the most used figures of speech to manifest
negative opinions is the sarcasm. In fact, it
is used to disguise and, at the same time, to
reinforce the negative thinking, such as:
i) Un pensiero di ringraziamento ogni mat-
tina va sempre ai comunisti che ce li
hanno portati fino a casa musulmani
rom e delinquenti grazie7.
The ironic sharpness of the sarcasm seems
to be appropriated to express contempt and
to offend individuals subtly. In order to
study this correlation between sarcasm and
hate speech, we proposed the shared task
IronITA8at Evalita 2018 that asks partici-
pants to recognize ironic and sarcastic tweets
in a dataset containing also offensive mes-
sages addressed, especially, immigrants (San-
guinetti et al., 2018).
Moreover, we participated in two tasks
proposed at IberEval 2018 about hate speech:
aggressiveness detection in Mexican Spanish
tweets (MEX-A3T)9organized by ´
Alvarez-
Carmona et al. (2018) and identification
of misogynistic English and Spanish tweets
(AMI)10 organized by Fersini, Anzovino, and
Rosso (2018). As a confirmation of our in-
tuition, the systems proposed for these tasks
show some difficulties to classify the sarcas-
tic abusive tweets. Indeed, sarcasm, inde-
pendently from the differences between lan-
guages, disguises the real intention of the
message which is with difficulty recognized
by machine. In line with these early experi-
ments, IronITA could be a good step of anal-
ysis.
php?id=tasks
7Each morning, I would like to thank communists
who bring home musulmans, roms and delinquents
thanks. Tweet from IronITA corpus.
8http://di.unito.it/ironita18
9https://mexa3t.wixsite.com/home/
aggressive-detection-track
10https://amiibereval2018.wordpress.com/
The rest of the paper is structured as
follows. Section 2 introduces the literature
that inspired our investigation. Section 3
describes our participation in IberEval tasks
with the used approach and obtained results.
In Section 4 we analyze the presence of sar-
casm in analyzed aggressive and offensive
texts. Finally, in Section 5 and 6 we draw
our research proposal and the future work.
2 Related work
The literature about hate speech detection
includes different issues, such as: cyberbul-
lying, misogyny, nastiness and aggressive-
ness. The most commercial methods, cur-
rently, rely on the use of blacklists. However,
filtering the messages in this way does not
provide a sufficient remedy because it falls
short when the meaning is more subtle or al-
tered by sarcasm. Actually, some authors,
such as Justo et al. (2014) and Nobata et
al. (2016), underline the fact that sarcasm
makes the interpretation of the message dif-
ficult, generally requiring world knowledge.
Also Smokey, one of the first systems, im-
plemented by Spertus (1997), uses syntactic
and semantic rules with lexicons to recognize
flames.
In this context, the research is ori-
ented at investigating deeply the language
using classical (Samghabadi et al., 2017)
and deep learning methods (Del Vigna et
al., 2017). Differently from Mehdad and
Tetreault (2016) and Gamb¨ack and Sikdar
(2017), for MEX-A3T task in Frenda and
Banerjee (2018) we applied an experimental
technique that combines linguistic features
and Convolutional Neural Network (CNN).
For the first time, Anzovino, Fersini, and
Rosso (2018) propose a classical machine
learning approach to identify misogyny in
English, comparing different classifiers. Tak-
ing into account this previous work and the
psychological studies about sexism (Ford and
Boxer, 2011), in Frenda and Ghanem (2018)
we combined sentiment and stylistic informa-
tion with specific lexicons involving several
aspects of misogyny online.
In the following section we report how we
addressed the identification of aggressiveness
and misogyny in Twitter, the experiments
carried out and the results obtained.
14
3 Hate speech, aggressiveness
and misogyny
Considering our motivations, our early
experiments focus mainly on hate speech
detection. For this purpose, we participated
at two tasks at IberEval 2018 respectively
about aggressiveness and misogyny detec-
tion.
3.1 Aggressiveness detection
The first task aims to classify aggressive and
non-aggressive tweets in Mexican Spanish.
We applied a deep learning approach incor-
porating into CNN architecture a set of lin-
guistic features (DL+FE) concerning: proper
characteristics of a tweet, such as emoticons,
abbreviations and slang words; stylistic infor-
mation, such as the length of tweets, the use
of the punctuation and the uppercase charac-
ters; bags of words weighted with tf-idf; emo-
tive traits of the aggressiveness; and deroga-
tory adjectives and vulgar expressions typical
of Mexican culture.
By means of Information Gain, we no-
ticed that anger and disgust are the princi-
pal emotions that incite the aggressive be-
haviour. We compared this system with a
simple CNN architecture (DL) in order to un-
derstand the contribution of features to deep
learning approach. The measure used for the
competition is F-score for positive class (i.e.
aggressive class). Despite the novel approach,
the results obtained are low and the features
seem not to help deep learning, as showed in
Table 1.
Prec. Rec. F-pos Rank
DL 0.34 0.34 0.34 9
DL+FE 0.27 0.38 0.31 10
Table 1: Results for aggressiveness detection
Therefore, in order to understand what
are the difficulties of DL+FE, we carried
out the error analysis. We mainly noticed
that there are several humorous cases,
especially sarcastic (see Section 4), which
are misclassified.
3.2 Automatic misogyny
identification
The second task proposes to identify misog-
yny in two collection of English and Span-
ish tweets. In the case a tweet is classified
as misogynistic (Task A), we need to distin-
guish (Task B) if the target is an individual
or not (Tar.) and identify the type of misog-
yny, according to the following classes (Cat.):
stereotype and objectification, dominance,
derailing, sexual harassment and threats of
violence, and discredit. This subdivision of
misogyny allows us to explore the different
aspects of misogyny and compare them in
two different languages. Moreover, the data
are not geolocalized. Therefore, in order to
gather the linguistic variations and consider
the various traits of misogyny, we proposed
an approach based on stylistic features cap-
tured by means of the character n-grams, sen-
timent and affective information, and on a set
of lexicons concerning: sexuality, profanity,
femininity, human body and stereotypes. In
addition, we considered slangs, abbreviations
and hashtags.
By means of Information Gain, we discov-
ered some differences between the two lan-
guages: sexual language is more used in En-
glish misogynistic tweets, whereas profanities
or vulgarities are more used in Spanish ones.
For this task, we applied Support Vector Ma-
chine (SVM) and majority voting technique.
To evaluate the Task A the organizers used
Accuracy measure and for Task B the average
Macro-F1 measure. In Table 2 and Table 3
we report the promising results obtained with
better runs for both languages.
Approach Acc Rank
En Ensemble 0.87 2
Sp Ensemble 0.81 3
Table 2: Results for Task A of misogyny de-
tection
Approach F1 Cat. Tar. Rank
En SVM 0.44 0.29 0.59 1
Sp Ensemble 0.44 0.33 0.55 2
Table 3: Results for Task B of misogyny de-
tection
4 Sarcasm
In Trait´e des tropes (1729) Dumarsais has de-
fined the sarcasm as an ironie faite avec ai-
15
greur et emportement11, that is a kind of ag-
gressive and sharp irony addressed a target to
hurt or criticize him without to exclude the
possibility to amuse. This statement is cor-
roborate by our analyses on English, Spanish,
Mexican and Italian hate speech corpora. As
said above, we carried out the error analysis
for both tasks.
In the first competition we noticed that
our approach fails in the classification of sar-
castic aggressive utterances, such as:
ii) @USUARIO #LOS40MeetAndGreet 9 .
Por q es una mam´a luchona que cuida a
su bendici`on12.
Actually, the sarcasm is a type of figurative
devices that modifies the perception of mes-
sage, hindering the correct detection of hate
speech by automatic systems. We found, in
fact, the same difficulty for the recognition of
misogynistic tweets in both languages, such
as:
iii) ¿Cu´al es la peor desgracia para una mu-
jer? Parir un var´on, porque despu´es de
tener un cerebro dentro durante 9 meses,
van y se lo sacan13;
iv) What’s the difference between a blonde
and a washing machine? A washing ma-
chine won’t follow you around all day af-
ter you drop a load in it.
In virtual as in real life, sexist jokes are
very common. In general, they are considered
innocent by the majority of people. How-
ever, Ford and Boxer (2011) reveal that sex-
ist jokes are experienced by women as sex-
ual harassment as well as offences. Moreover,
Ford, Wentzel, and Lorion (2001) investigate
on the effects of exposure to sexist jokes and
they underline that a continue exposition can
also modify the perception of sexism as norm
and not as misbehavior.
5 Research Proposal
These early observations suggest the neces-
sity to address the use of figures of speech
such as sarcasm, in order to accurate, in
11“type of irony done with sharpness and a fit of
anger”
12@User #LOS40MeetAndGreet 9 . Because she is
a fighter mother who takes care of her kid.
13What’s the worst disgrace for a woman? Giving
birth to boy, because after she has got a brain into her
for 9 months, it is taken out
a multilingual perspective, the automated
methods to flag abusive language.
For this purpose, we propose an accurate
analysis of different kinds of hate speech on-
line especially in Italian, English and Span-
ish, taking into account also the geographical
linguistic variations. We focus in particular
on short texts such as tweets, posts or com-
ments, exploring the informal language.
Considering the previous observations, we
propose approaching the hate speech detec-
tion issues taking into account the figurative
dimension of language and especially of abu-
sive language. Moreover, it is necessary to
examine the appropriateness of various com-
putational techniques to solve this problem.
In this line, we want to examine the contribu-
tion of the linguistic features to deep learning
approaches by comparison with the perfor-
mances of classical techniques. Finally, the
multilingual context allows to discover the
typical aspects of hate speech in order to rec-
ognize it independently from the languages.
Indeed, the scope of this investigation is to
propose a methodology for monitoring cor-
rectly the user-generated contents allowing
the system to work as sensor of the violence,
also in real world.
6 Future work
Our research aims to explore the several di-
mensions of hate speech considering, above
all, the use of figurative devices that hinder
the automatic processes of recognition. In
order to investigate the remarks observed in
these first experiments, as future work, we
would like to participate in HaSpeeDe14 and
AMI15 at Evalita 2018 for Italian.
In addition, similar tasks are proposed at
SemEval 2019 concerning: multilingual hate
speech against immigrants and women (Hat-
Eval)16, and the identification and catego-
rization of offensive language in social me-
dia (OffensEval)17. Analyzing different kinds
of abusive language allows to understand the
boundaries between them and their singular
aspects. Finally, multilingual context gives
us the opportunity to delineate the differ-
14http://www.di.unito.it/~tutreeb/
haspeede-evalita18/index.html\#
15https://amievalita2018.wordpress.com/
16https://competitions.codalab.org/
competitions/19935
17https://competitions.codalab.org/
competitions/20011
16
ences and analogies between the various lan-
guages, inferring general characteristics of
hate speech online.
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17
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... Sarcasm and irony As has been shown in multiple studies, e.g., [8], hateful messages containing sarcasm or irony are problematic for detection. Even though this only occurs in 3% and 4% of the explicit and implicit categories respectively, it is worth pointing out. ...
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Over the past years, the amount of online hate speech has been growing steadily. Among multiple approaches to automatically detect hateful content online, ensemble learning is considered one of the best strategies, as shown by several studies on English and other languages. In this paper, we evaluate state-of-the-art approaches for Dutch hate speech detection both under in-domain and cross-domain hate speech detection conditions, and introduce a new ensemble approach with additional features for detecting hateful content in Dutch social media. The ensemble consists of the gradient boosting classifier that incorporates state-of-the-art transformer-based pre-trained language models for Dutch (i.e., BERTje and RobBERT), a robust SVM approach, and additional input information such as the number of emotion-conveying and hateful words, the number of personal pronouns, and the length of the message. The ensemble significantly outperforms all the individual models both in the in-domain and cross-domain hate speech detection settings. We perform an in-depth error analysis focusing on the explicit and implicit hate speech instances, providing various insights into open challenges in Dutch hate speech detection and directions for future research.
... This can affect the results of our model and is therefore, a limitation. Moreover, sarcasm [18] is difficult to detect in text. Since we have not explicitly handled sarcasm, some of the false positives (FP) that we encountered in our model were classified as cyberbullying but were actually normal tweets. ...
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Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, cyberbullying amongst children and other vulnerable groups are only growing over online classes, and increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure minors' safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism, abusive, and hate-speech. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus, achieving a higher precision in comparison to models without slang preprocessing.
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Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, and cyberbullying amongst vulnerable groups is only growing over increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism, abusive, and hate-speech. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus, achieving a higher precision in comparison to models without slang preprocessing.
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