=Paper=
{{Paper
|id=Vol-2263/paper034
|storemode=property
|title=CrotoneMilano for AMI at Evalita2018. A Performant, Cross-lingual Misogyny Detection System.
|pdfUrl=https://ceur-ws.org/Vol-2263/paper034.pdf
|volume=Vol-2263
|authors=Angelo Basile,Chiara Rubagotti
|dblpUrl=https://dblp.org/rec/conf/evalita/BasileR18
}}
==CrotoneMilano for AMI at Evalita2018. A Performant, Cross-lingual Misogyny Detection System.==
https://ceur-ws.org/Vol-2263/paper034.pdf
CrotoneMilano for AMI at Evalita2018.
A performant, cross-lingual misogyny detection system.
Angelo Basile Chiara Rubagotti
Symanto Research Independent Researcher
angelo.basile@symanto.net chiara.rubagotti@gmail.com
Abstract 1 Introduction
We present our systems for misogyny With awareness of violence against women grow-
identification on Twitter, for Italian and ing in the public discourse and the spread of un-
English. The models are based on a Sup- filtered and possibly anonymous communication
port Vector Machine and they use n-grams on social media in our digital culture, the issue
as features. Our solution is very simple of misogyny online has become compelling. Vi-
and yet we achieve top results on Ital- olence against women has been described by the
ian Tweets and excellent results on En- UN as a “Gender-based [..] form of discrimi-
glish Tweets. Furthermore, we exper- nation that seriously inhibits women’s ability to
iment with a single model that works enjoy rights and freedoms on a basis of equal-
across languages by leveraging abstract ity with men”1 . On the web this often takes the
features. We show that a single multi- form of female-discriminating attacks of differ-
lingual system yields performances com- ent types, which undermine the women’s rights
parable to two independently trained sys- of freedom of expression and participation2 . Fol-
tems. We achieve accuracy results ranging lowing erjavec2012you
¸ ’s understanding of hate
from 45% to 85%. Our system is ranked speech, reported in (Pamungkas et al., 2018) as
first out of twelve submissions for sub-task “any type of communication that is abusive, in-
B on Italian and second for sub-task A. sulting, intimidating, harassing, and/or incites to
violence or discrimination, and that disparages a
In questo articolo presentiamo i nostri mo- person or a group on the basis of some charac-
delli per il riconoscimento automatico di teristics such as race, colour, ethnicity, gender,
testi misogini su Twitter: abbiamo adde- sexual orientation, nationality, religion, or other
strato lo stesso sistema prima su un corpus characteristics”, we can define misogynist speech
italiano e poi su uno inglese. Il modello si as any kind of aggressive discourse which tar-
basa su una macchina a vettori supporto gets women because they are women. Within the
e usa n-grammi come feature. La nostra larger context of hate speech, online misogyny —
soluzione è molto semplice e tuttavia ci or cybersexism — stands out as a large and com-
permette di raggiungere lo stato dell’arte plex phenomenon which reflects other forms of of-
sull’italiano e ottimi risultati sull’inglese. fline abuse on women (Poland, 2016). This holds
Presentiamo inoltre un sistema che funzio- true for the Italian case as well, where bouts of
na con entrambe le lingue sfruttando una misogynistic tweets have been linked to episodes
serie di feature astratte. Il nostro livello of femicides 3 . In recent years the NLP commu-
raggiunge livelli di accuratezza tra il 45%
1
e l’85%: con questi risultati ci piazziamo http://www.un.org/womenwatch/daw/cedaw/recommendations/recomm.htm
2
https://www.amnesty.org/en/latest/research/2018/03/online-
primi nel task B per l’italiano e secondi violence-against-women-chapter-3/.
nel task A. 3
http://www.voxdiritti.it/wp-
content/uploads//2018/06/mappa-intolleranza-3-donne.jpg
nity has addressed the issue of automatic detec- B). The misogynistic behaviour’s space consists of
tion of hate speech in general (Schmidt and Wie- five different labels:
gand, 2017) and misogyny in particular (Anzovino
et al., 2018). This effort to detect and contain ver- • Stereotype & Objectification
bal violence on social media (or any kind of text)
• Dominance
demonstrates how NLP tools can also be used for
ethically beneficial purposes and should be con- • Derailing
sidered in the newborn and crucial discourse on
Ethics in NLP (Hovy and Spruit, 2016; Hovy et • Sexual Harassment & Threats of
al., 2017; Alfano et al., 2018). We are therefore Violence
proud to take up the AMI challenge (Fersini et al.,
• Discredit
2018) and present our contribution to the cause
of stopping misogynistic speech on Twitter. In The target can be either Active when the mes-
this paper we propose a simple linear model us- sage refers to a specific person or Passive when
ing n-grams: we show that such a simple setup the message expresses generic misogyny. The
can still yield good results. We decided to pro- setup is the same for both Italian and English.
pose a simple model for three reasons: first, it
has been shown that linear SVM can easily outper- 2 Data
form more complex deep neural networks (Plank,
We use only the data released by the task organ-
2017; Medvedeva et al., 2017); second, training
isers: they consist of Italian and English Tweets.
and testing our model does not require expensive
The organisers report that the corpus has been
hardware but a common laptop is enough to repli-
manually labelled by several annotators. We pro-
cate our experiments; third, we experiment with a
vide an overview of the data set in Table 1. As it
transformation of the input (i.e. we extract abstract
can be seen from the table, the data for Task A is
features) and a linear model allows for an easier
more or less balanced, while the data for Task B is
interpretation of the contribution of this transfor-
highly skewed.
mation.
To summarise, the following are the contribu- 3 Experiments
tions of this paper:
In this section we describe the feature extraction
• We propose a simple and yet strong misog- process and the model that we built.
yny detection system for English and Italian
(ranked first out of twelve systems for misog- 3.1 Pre-processing
ynistic category detection) We decide not to pre-process the data in any way,
since we do not have linguistic (or non-linguistic)
• We show how a single system can be trained reasons for doing so. To tokenize the text we sim-
to work across languages ply split at every white space.
• We release all the code4 and our trained 3.2 Model and Features
systems for reproducibility and for a quick We built a sparse linear model for approaching this
implementation of language technology sys- task.
tems that can help detect and mitigate cyber- We use n-grams extracted at the word level as
sexism phenomena. well as at the character level. We use 3-10 n-
grams and binary tf-idf. We feed these features
Task Description The AMI task is combined to a Support Vector Machine (SVM) model with a
binary and multi-label, short text classification linear kernel; we use the implementation included
task. Given a Tweet, we have to predict whether it in scikit-learn (Pedregosa et al., 2011). Fur-
contains or not misogyny (Task A) and if it does, thermore, we experiment with feature abstraction:
we have to classify the misogynistic behaviour and we follow the bleaching approach recently pro-
predict who is the subject being targeted (Task posed by (van der Goot et al., 2018). First, we
4
The code can be found at transform each word in a list of symbols that 1)
https://github.com/anbasile/AMI/. represents the shape of the individual characters
I TALIAN E NGLISH
SO DO DE ST DI SO DO DE ST DI
Active 625 61 21 428 586 54 78 24 207 695
Passive 40 9 3 2 43 125 70 68 145 319
Non-Misogynous 2172 2215
Table 1: Data Set Overview, showing the label distribution across the five misogynistic behaviours:
Stereotype & Objectification (SO), Dominance (DO), Derailing (DE), Sexual Harassment & Threats of
Violence (ST) and Discredit (DI).
SHAPE FREQ LEN ALPHA the official test set, using the gold labels released
by the organisers after the evaluation period.
This Ccvc 46 04 True
is vc 650 02 True 4 Evaluation and Results
an vc 116 02 True
example vcvcccv 1 07 True Since the data set labels for the sub-task B are not
. . 60 01 False evenly distributed across the classes, we use f1-
1 01 False score to evaluate our model. First we report re-
sults obtained via a 10-fold cross-validation on the
Table 2: An illustration of the bleaching process. training set; then, we report results from the offi-
cial test set, whose labels have been released. The
official evaluation does not take into account the
and 2) abstracts from meaning by still approxi- joint prediction of the labels, however here we re-
mating the vowels and characters that compose the port results considering the 0 label: since we train
word; then, we compute the length of the word and different models for the different label sets, we
its frequency (while taking care of padding the first make sure that the models trained on Task B are
one with a zero in order to avoid feature collision); able to detect if a message is misogynistic in the
finally, we use a Boolean label for explicitly dis- first place.
tinguishing words from non-alphanumeric token
(e.g. emojis). Table 2 shows an example of this 4.1 Development Results
feature abstraction process. We report the development results obtained by us-
(van der Goot et al., 2018) proposed this bleach- ing different text representations. Table 3 presents
ing approach for modelling gender across lan- an overview of these results. We note that all four
guages, by leveraging the language-independent representations — words, characters, a combina-
nature of these features: here, we try to re-use the tion of these two and the bleached representation
technique for classifying misogynist text across — all yield comparable results. The combination
languages. We slightly modify the representation of words and characters seems to be the best for-
proposed by (van der Goot et al., 2018) by merg- mat. Overall, we note that the system performs
ing the shape feature (e.g. Xxx) with the vowel- better on the Italian corpus than on the English
consonant approximation feature (e.g. CVC) into corpus.
one single feature (e.g. Cvc).
We propose three different multi-lingual exper- 4.1.1 Cross-lingual Results
iments: In Table 4 we present the results of our cross-
lingual experiments. We train and test different
• TRAIN Italian → TEST English systems using lexical and abstract features. We
note that the abstract model trained on Italian out-
• TRAIN English → TEST Italian
performs the fully lexicalized model when tested
• TRAIN Ita. & Eng. → TEST Ita. & Eng. on English, but the opposite is not true. The En-
glish data set seems particularly hard for both the
For the last experiment, we use half the data set abstract and the lexicalized model. Interestingly,
for each language. We report scores obtained by the abstract model trained on both corpora shows
training on the whole training set and testing on good results.
E NGLISH I TALIAN
MIS . CAT. TGT. MIS . CAT. TGT.
Words (W) 0.68 0.29 0.57 0.88 0.60 0.59
Chars (C) 0.71 0.30 0.61 0.88 0.59 0.58
W+C 0.70 0.31 0.59 0.88 0.62 0.59
Bleaching 0.68 0.27 0.57 0.85 0.55 0.56
Table 3: An overview of the development f1-macro scores obtained via cross-validation.
TEST → IT EN the big difference in performance between the En-
glish and Italian models, we show the importance
IT lex 0.85 0.51
of words as learned by the model: we print the
IT abs 0.83 0.52
ten most important words, ranked by their learned
TRAIN
EN lex 0.47 0.62
weights. The result is shown in Table 6. From
EN abs 0.45 0.52
the output we see that the model trained on Italian
IT + EN lex 0.83 0.60
learned meaningful words for identifying a misog-
IT + EN abs 0.81 0.58
ynist message, such as zitta [shut up], tua [your]
and muori [die!]: these words stand out from the
Table 4: Pair-wise accuracy results for Task A.
rest of the profanity for directly referring to some-
We compare lexicalized vs. abstract models. The
one, while the rest of the words and almost all the
combined IT+EN data set is built by randomly
most important English words could be used as in-
sampling 50% of instances from both corpora.
terjections or could be more generic insults.
4.2 Test Results
RANK ITA ENG
In Table 5 we present official test results (Fersini
et al., 2018). We submitted only one, constrained 1 zitta woman
run; a run is considered constrained when only 2 bel hoe
the data released by the organisers are used. We 3 pompinara she
submitted the model using the combined represen- 4 puttanona hoes
tation with word- and character-ngrams, trained 5 tua women
once on the English corpus and once on the Ital- 6 muori whore
ian corpus. We achieve the top and the second po- 7 baldracca her
sition for the tasks B and A respectively on the 8 troie bitches
Italian data set. On the English data set our sys- 9 culona womensuck
tem is ranked 15th and 4th on the tasks A and B 10 tettona bitch
respectively.
Table 6: Top ten words ranked by their positive
weights learned during training.
TASK A TASK B
CATEGORY TARGET AVG.
The results of the abstract system are satisfac-
IT 0.843 0.579 0.423 0.501
tory for eventually building a light, portable model
EN 0.617 0.293 0.444 0.369
that could be adapted to different language. In the
Table 5: Official test results. Task A is measured future we will try training on English and Italian
using accuracy and Task B is measured using f1- and testing on a third corpus (such as the Spanish
score. We reach the first position on Task B for version of the AMI data set).
Italian. In this paper we described our participation to
the AMI - Automatic Misogyny Identification for
Italian and English. We proposed a very simple
5 Discussion and Conclusions
solution that can be implemented quickly and we
A warning to the reader: this section contains ex- scored a state-of-the-art result for classification of
plicit language. In the attempt to understand better misogynistic behaviours in five classes.
Acknowledgements F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel,
B. Thirion, O. Grisel, M. Blondel, P. Pretten-
The authors would like to thank the two anony- hofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Pas-
mous reviewers who helped improve the quality of sos, D. Cournapeau, M. Brucher, M. Perrot, and
this paper. The first author has conducted this re- E. Duchesnay. 2011. Scikit-learn: Machine learn-
ing in Python. Journal of Machine Learning Re-
search as he was still part of the Erasmus Mundus search, 12:2825–2830.
master in Language and Communication Technol-
ogy, a shared master program between the Uni- Barbara Plank. 2017. All-in-1 at ijcnlp-2017 task 4:
Short text classification with one model for all lan-
versity of Groningen (NL) and the University of guages. Proceedings of the IJCNLP 2017, Shared
Malta (MT). Tasks, pages 143–148.
Bailey Poland. 2016. Haters: Harassment, Abuse, and
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