=Paper=
{{Paper
|id=Vol-2517/T3-9
|storemode=property
|title=HateMonitors: Language Agnostic Abuse Detection in Social Media
|pdfUrl=https://ceur-ws.org/Vol-2517/T3-9.pdf
|volume=Vol-2517
|authors=Punyajoy Saha,Binny Mathew,Pawan Goyal,Animesh Mukherjee
|dblpUrl=https://dblp.org/rec/conf/fire/SahaMG019
}}
==HateMonitors: Language Agnostic Abuse Detection in Social Media==
https://ceur-ws.org/Vol-2517/T3-9.pdf
HateMonitors: Language Agnostic Abuse
Detection in Social Media
Punyajoy Saha1[0000−0002−3952−2514] , Binny Mathew1[0000−0003−4853−0345] ,
Pawan Goyal1[0000−0002−9414−8166] , and Animesh
Mukherjee1[0000−0003−4534−0044]
Indian Institute of Technology, Kharagpur, West Bengal, India - 721302
Abstract. Reducing hateful and offensive content in online social me-
dia pose a dual problem for the moderators. On the one hand, rigid
censorship on social media cannot be imposed. On the other, the free
flow of such content cannot be allowed. Hence, we require efficient abu-
sive language detection system to detect such harmful content in social
media. In this paper, we present our machine learning model, HateMon-
itor, developed for Hate Speech and Offensive Content Identification in
Indo-European Languages (HASOC) [20], a shared task at FIRE 2019.
We have used Gradient Boosting model, along with BERT and LASER
embeddings, to make the system language agnostic. Our model came
at First position for the German sub-task A. We have also made our
model public 1 .
Keywords: Hate speech · Offensive language · Multilingual · LASER
embeddings · BERT embeddings · Classification.
1 Introduction
In social media, abusive language denotes a text which contains any form of
unacceptable language in a post or a comment. Abusive language can be divided
into hate speech, offensive language and profanity. Hate speech is a derogatory
comment that hurts an entire group in terms of ethnicity, race or gender. Of-
fensive language is similar to derogatory comment, but it is targeted towards
an individual. Profanity refers to any use of unacceptable language without a
specific target. While profanity is the least threatening, hate speech has the most
detrimental effect on the society.
Social media moderators are having a hard time in combating the rampant
spread of hate speech2 as it is closely related to the other forms of abusive
language. The evolution of new slangs and multilingualism, further adding to
the complexity.
1
https://github.com/punyajoy/HateMonitors-HASOC
2
https://tinyurl.com/y6tgv865
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). FIRE 2019, 12-15 Decem-
ber 2019, Kolkata, India.
P. Saha et al.
Recently, there has been a sharp rise in hate speech related incidents in India,
the lynchings being the clear indication [3]. Arun et al. [3] suggests that hate
speech in India is very complicated as people are not directly spreading hate
but are spreading misinformation against a particular community. Hence, it has
become imperative to study hate speech in Indian language.
For the first time, a shared task on abusive content detection has been re-
leased for Hindi language at HASOC 2019. This will fuel the hate speech and
offensive language research for Indian languages. The inclusion of datasets for
English and German language will give a performance comparison for detection
of abusive content in high and low resource language.
In this paper, we focus on the detection of multilingual hate speech detection
that are written in Hindi, English, and German and describe our submission
(HateMonitors) for HASOC at FIRE 2019 competition. Our system concate-
nates two types of sentence embeddings to represent each tweet and use machine
learning models for classification.
2 Related works
Analyzing abusive language in social media is a daunting task. Waseem et al. [33]
categorizes abusive language into two sub-classes – hate speech and offensive lan-
guage. In their analysis of abusive language, Classifying abusive language into
these two subtypes is more challenging due to the correlation between offensive
language and hate speech [10]. Nobata et al. [22] uses predefined language el-
ement and embeddings to train a regression model. With the introduction of
better classification models [23, 29] and newer features [1, 10, 30], the research in
hate and offensive speech detection has gained momentum.
Silva et al. [28] performed a large scale study to understand the target of
such hate speech on two social media platforms: Twitter and Whisper. These
target could be the Refugees and Immigrants [25], Jews [7, 14] and Muslims [4,
32]. People could become the target of hate speech based on Nationality [12],
sex [5, 26], and gender [24, 16] as well. Public expressions of hate speech affects
the devaluation of minority members [17], the exclusion of minorities from the
society [21], and tend to diffuse through the network at a faster rate [19].
One of the key issues with the current state of the hate and offensive language
research is that the majority of the research is dedicated to the English language
on [15]. Few researchers have tried to solve the problem of abusive language in
other languages [25, 27], but the works are mostly monolingual. Any online social
media platform contains people of different ethnicity, which results in the spread
of information in multiple languages. Hence, a robust classifier is needed, which
can deal with abusive language in the multilingual domain. Several shared tasks
like HASOC [20], HaSpeeDe [8], GermEval [34], AMI [13], HatEval [6] have
focused on detection of abusive text in multiple languages recently.
HateMonitors: Language Agnostic Abuse Detection in Social Media
3 Dataset and Task description
The dataset at HASOC 2019 were given in three languages: Hindi, English, and
German. Dataset in Hindi and English had three subtasks each, while German
had only two subtasks. We participated in all the tasks provided by the organisers
and decided to develop a single model that would be language agnostic. We used
the same model architecture for all the three languages.
3.1 Datasets
We present the statistics for HASOC dataset in Table 1. From the table, we can
observe that the dataset for the German language is highly unbalanced, English
and Hindi are more or less balanced for sub-task A. For sub-task B German
dataset is balanced but others are unbalanced. For sub-task C both the datasets
are highly unbalanced.
Table 1. This table shows the initial statistics about the training and test data
Language English German Hindi
Sub-Task A Train Test Train Test Train Test
HOF 2261 288 407 136 2469 605
NOT 3591 865 3142 714 2196 713
Total 5852 1153 3819 850 4665 1318
Sub-Task B Train Test Train Test Train Test
HATE 1141 124 111 41 556 190
OFFN 451 71 210 77 676 197
PRFN 667 93 86 18 1237 218
Total 2261 288 407 136 2469 605
Sub-Task C Train Test Train Test Train Test
TIN 2041 245 - - - - 1545 542
UNT 220 43 -- -- 924 63
Total 2261 288 - - - - 2469 605
3.2 Tasks
Sub-task A consists of building a binary classification model which can predict
if a given piece of text is hateful and offensive (HOF) or not (NOT). A data
point is annotated as HOF if it contains any form of non-acceptable language
such as hate speech, aggression, profanity. Each of the three languages had this
subtask.
Sub-task B consists of building a multi-class classification model which can
predict the three different classes in the data points annotated as HOF: Hate
https://hasoc2019.github.io/
P. Saha et al.
speech (HATE), Offensive language (OFFN), and Profane (PRFN). Again all
three languages have this sub-task.
Sub-task C consists of building a binary classification model which can predict
the type of offense: Targeted (TIN) and Untargeted (UNT). Sub-task C was not
conducted for the German dataset.
4 System Description
In this section, we will explain the details about our system, which comprises
of two sub-parts- feature generation and model selection. Figure 1 shows the
architecture of our system.
4.1 Feature Generation
Preprocessing: We preprocess the tweets before performing the feature ex-
traction. The following steps were followed:
– We remove all the URLs.
– Convert text to lowercase. This step was not applied to the Hindi language
since Devanagari script does not have lowercase and uppercase characters.
– We did not normalize the mentions in the text as they could potentially
reveal important information for the embeddings encoders.
– Any numerical figure was normalized to a string ‘number’.
We did not remove any punctuation and stop-words since the context of the
sentence might get lost in such a process. Since we are using sentence embedding,
it is essential to keep the context of the sentence intact.
Feature vectors: The preprocessed posts are then used to generate features for
the classifier. For our model, we decided to generate two types of feature vector:
BERT Embeddings and LASER Embeddings. For each post, we generate the
BERT and LASER Embedding, which are then concatenated and fed as input
to the final classifier.
Multilingual BERT embeddings: Bidirectional Encoder Representations
from Transformers(BERT) [11] has played a key role in the advancement of
natural language processing domain (NLP). BERT is a language model which
is trained to predict the masked words in a sentence. To generate the sentence
embedding for a post, we take the mean of the last 11 layers (out of 12) to get
a sentence vector with length of 768.
LASER embeddings: Researchers at Facebook released a language agnos-
tic sentence embeddings representations (LASER) [2], where the model jointly
learns on 93 languages. The model takes the sentence as input and produces a
vector representation of length 1024. The model is able to handle code mixing
as well [31].
We use the BERT-base-multilingual-cased which has 104 languages, 12-layer, 768-
hidden, 12-heads and 110M parameters
HateMonitors: Language Agnostic Abuse Detection in Social Media
Labels Sentences
Pre-processing module
LASER Multilingual BERT
sentence sentence
embedding embedding
1024 X 1 Concatenate 768 X 1
LGBM
Fig. 1. Architecture of our system
We pass the preprocessed sentences through each of these embedding mod-
els and got two separate sentence representation. Further, we concatenate the
embeddings into one single feature vector of length 1792, which is then passed
to the final classification model.
4.2 Our Model
The amount of data in each category was insufficient to train a deep learning
model. Building such deep models would lead to overfitting. So, we resorted to
using simpler models such as SVM and Gradient boosted trees. Gradient boosted
trees [9] are often the choice for systems where features are pre-extracted from
the raw data. In the category of gradient boosted trees, Light Gradient Boosting
Machine (LGBM) [18] is considered one of the most efficient in terms of memory
footprint. Moreover, it has been part of winning solutions of many competition
. Hence, we used LGBM as model for the downstream tasks in this competition.
5 Results
The performance of our models across different languages for sub-task A are
shown in table 2. Our model got the first position in the German sub-task
with a macro F1 score of 0.62. The results of sub-task B and sub-task C is
shown in table 3 and 4 respectively.
https://tinyurl.com/yxmuwzla
https://tinyurl.com/y2g8nuuo
P. Saha et al.
Table 2. This table gives the language wise result of sub-task A by comparing the
macro F1 values
Language English German Hindi
HOF 0.59 0.36 0.76
NOT 0.79 0.87 0.79
Total 0.69 0.62 0.78
Table 3. This table gives the language wise result of sub-task B by comparing the
macro F1 values
Language English German Hindi
HATE 0.28 0.04 0.29
OFFN 0.00 0.0 0.29
PRFN 0.31 0.19 0.59
NONE 0.79 0.87 0.79
Total 0.34 0.28 0.49
Table 4. This table gives the language wise result of sub-task C by comparing the
macro F1 values
Language English Hindi
TIN 0.51 0.63
UNT 0.11 0.17
NONE 0.79 0.79
Total 0.47 0.53
6 Discussion
In the results of subtask A, models are mainly affected by imbalance of the
dataset. The training dataset of Hindi dataset was more balanced than English
or German dataset. Hence, the results were around 0.78. As the dataset in
German language was highly imbalanced, the results drops to 0.62. In subtask
B, the highest F1 score reached was by the profane class for each language in
table 3. The model got confused between OFFN, HATE and PRFN labels which
suggests that these models are not able to capture the context in the sentence.
The subtask C was again a case of imbalanced dataset as targeted(TIN) label
gets the highest F1 score in table 4.
7 Conclusion
In this shared task, we experimented with zero-shot transfer learning on abusive
text detection with pre-trained BERT and LASER sentence embeddings. We
use an LGBM model to train the embeddings to perform downstream task. Our
model for German language got the first position. The results provided a strong
baseline for further research in multilingual hate speech. We have also made the
models public for use by other researchers.
https://github.com/punyajoy/HateMonitors-HASOC
HateMonitors: Language Agnostic Abuse Detection in Social Media
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