=Paper=
{{Paper
|id=Vol-3159/T4-4
|storemode=property
|title=Abusive and Threatening Language Detection from Urdu Social Media Posts: A machine learning approach
|pdfUrl=https://ceur-ws.org/Vol-3159/T4-4.pdf
|volume=Vol-3159
|authors=Abhinav Kumar,Sunil Saumya,Pradeep Kumar Roy
|dblpUrl=https://dblp.org/rec/conf/fire/KumarSR21
}}
==Abusive and Threatening Language Detection from Urdu Social Media Posts: A machine learning approach==
https://ceur-ws.org/Vol-3159/T4-4.pdf
Abusive and Threatening Language Detection from
Urdu Social Media Posts: A machine learning
approach
Abhinav Kumar1 , Sunil Saumya2 and Pradeep Kumar Roy3
1
Department of Computer Science & Engineering, Siksha ’O’ Anusandhan Deemed to be University, Bhubaneswar, India
2
Department: Computer Science & Engineering, Indian Institute of Information Technology Dharwad, India
3
Indian Institute of Information Technology Surat, Gujarat
Abstract
Urdu is spoken by approximately 230 million people throughout the world, and it has a sizable social
media and digital media following. However, none of the possible efforts to detect abusive and threatening
postings from Urdu social media posts has been suggested to the best of our knowledge. This study
explores the usability of conventional machine learning and deep learning models to identify abusive
and threatening messages on Urdu social media. The proposed ensemble-based machine learning model
performed promising in the identification of abusive and threatening language from Urdu social media
posts. The suggested ensemble-based model achieved a weighted 𝐹1 -score of 0.81, the accuracy of 0.81, a
ROC of 0.90 for abusive language identification, and a weighted 𝐹1 -score of 0.81, accuracy of 0.85, and
ROC of 0.81 for threatening language identification.
Keywords
Abusive content, Social media, Deep learning, Hate speech,
1. Introduction
The impact of social media platform misuse has grown in tandem with the expansion and
prominence of these platforms [1, 2, 3]. Numerous posts, in the example, contain abusive
language directed at specific users, therefore detracting from the communication experience on
such platforms, while others contain genuine threats that might put platform users in danger [4, 5,
6, 7, 8]. Several works [9, 10, 11, 12, 13, 14, 15] have been proposed by researchers to identify hate
speech from English, Hindi, and code-mixed Dravidian social media posts. Kumari and Singh
[13] presented a model based on convolutional neural networks for detecting hate, obscenity,
and abusive language in English and Hindi tweets. To recognize hatred, offensive, and profanity
in English, Hindi, and German tweets, Mishra and Pal [14] developed an attention-based
bidirectional long-short-term memory network. Mujadia et al. [15] developed an ensemble-
based model comprised of a support vector machine, random forest, and Adaboost classifiers
to identify hate content in tweets written in English, Hindi, and German. Saumya et al. [12]
experimented with several conventional machine learning and deep learning models for the
Forum for Information Retrieval Evaluation, December 13-17, 2021, India
Envelope-Open abhinavanand05@gmail.com (A. Kumar); sunil.saumya@iiitdwd.ac.in (S. Saumya); pradeep.roy@iiitsurat.ac.in
(P. K. Roy)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
hate speech identification from Dravidian social media posts. They found character N-gram
features with conventional machine learning classifiers performing better than the complex
deep learning models.
Urdu is spoken by over 230 million people worldwide, and it has a significant social media and
digital media presence. But to the best of our knowledge, none of the potential work has been
proposed to identify abusive and threatening posts from Urdu social media posts. This paper
explores the usages of different conventional machine learning and deep learning models for
the identification of abusive and threatening Posts from Urdu social media posts. The proposed
models are validated with the dataset published in CICLing 2021 track FIRE 2021 workshop
[16]. The task is divided into two subtasks: (i) Sub-task A is concerned with identifying the
abusive language in Twitter tweets written in Urdu. This is a binary classification task that
must classify tweets into one of two categories: abusive or non-abusive, (ii) Sub-task B focuses
on identifying Threatening language in Urdu-language tweets. This is a binary classification
task in that one must categorize tweets into two categories: Threatening and Non-Threatening.
The rest of the sections are organized as follows: Section 2 discusses the proposed methodol-
ogy in detail. Section 3 lists the findings and finally the paper concluded in Section 4.
2. Methodology
This section discusses the proposed methodology in detail. For Task-A: Abusive language
identification, we submitted four different models: (i) Ensemble (SVM + LR + RF) (Ensemble of
Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF)), (ii) Dense
Neural Network (DNN), (iii) Ensemble (Three variant of BERT ((a) BERT bert-base-arabic1 ,
(b) distilbert-base-multilingual-cased2 , and (c) bert-base-multilingual-cased3 ), and (iv) Support
Vector Machine (SVM).
To provide input to the models, one-to-six gram character-level TF-IDF features were used
for Ensemble (SVM + LR + RF), Dense Neural Network (DNN), and SVM models. As the
deep learning-based models are very sensitive to the chosen hyper-parameters, we performed
extensive experiments to choose the best-suited hyper-parameters for the proposed models.
After extensive experiments by varying the number of layers, learning rate, batch size, epochs,
loss function, and optimizer, for dense neural network (DNN) model, we found four layers with
4,096, 1,024, 128, and 2-neurons performed best with a dropout rate of 0.2, binary cross-entropy
as a loss function and Adam as the optimizer, a batch size of 32, and a learning rate of 0.001.
Similarly, for the Ensemble (Three variants of BERT), we found a learning rate of 2𝑒 −5 and a
batch size of 32 with 20 epochs of training performed best. After training each BERT model
individually, the prediction probability for both the classes was averaged class-wise to get the
final class prediction.
In the case of Task-B of threatening language identification, we submitted two models: (i)
Ensemble (SVM + LR + RF) and (ii) AdaBoost. In this case, also, we used one-to-six gram
character-level TF-IDF features to train the system.
1
https://huggingface.co/asafaya/bert-base-arabic
2
https://huggingface.co/distilbert-base-multilingual-cased
3
https://huggingface.co/bert-base-multilingual-cased
Table 1
Results of different models for Abusive and Threatening language identification
Task Models Class Precision Recall 𝐹1 -score Accuracy ROC
Task-A (Abusive) Ensemble (SVM + LR + RF) Not-Abusive 0.77 0.86 0.82 0.81 0.90
Abusive 0.85 0.76 0.80
Weighted Avg. 0.81 0.81 0.81
DNN Not-Abusive 0.75 0.81 0.78 0.78 0.83
Abusive 0.81 0.74 0.77
Weighted Avg. 0.78 0.78 0.78
Ensemble (Three variant of BERT) Not-Abusive 0.79 0.77 0.78 0.79 0.85
Abusive 0.78 0.80 0.79
Weighted Avg. 0.79 0.79 0.79
SVM Not-Abusive 0.76 0.87 0.81 0.80 0.89
Abusive 0.86 0.74 0.80
Weighted Avg. 0.81 0.80 0.80
Task-B (Threatening) Ensemble (SVM + LR + RF) Not-Threatening 0.85 0.99 0.91 0.85 0.81
Threatening 0.77 0.22 0.35
Weighted Avg. 0.84 0.85 0.81
AdaBoost Not-Threatening 0.85 0.96 0.90 0.83 0.74
Threatening 0.60 0.26 0.37
Weighted Avg. 0.81 0.83 0.81
Confusion matrix
450
400
Not-Abusive 0.86 0.14
350
300
True label
250
200
Abusive 0.24 0.76
150
100
e
ive
siv
us
bu
Ab
t-A
No
Predicted label
Figure 1: Confusion matrix of ensemble of SVM, LR, and RF classifiers for Abusive language detection
In the case of conventional machine learning classifier, Sklearn python library4 is used with
the default parameters. To implement dense neural network, Keras5 with TensorFlow6 as a
back-end whereas for BERT models, Huggingface7 library is used.
4
https://scikit-learn.org/stable/
5
https://keras.io/
6
https://www.tensorflow.org/
7
https://huggingface.co/
Confusion matrix
400
Not-Abusive 0.81 0.19 350
300
True label
250
0.26 0.74 200
Abusive
150
100
e
ive
siv
us
bu
Ab
t-A
No
Predicted label
Figure 2: Confusion matrix of Dense Neural Network (DNN) model for Abusive language detection
Confusion matrix
450
400
Not-Abusive 0.77 0.23
350
True label
300
250
Abusive 0.20 0.80 200
150
ive
ive
us
us
Ab
Ab
t-
No
Predicted label
Figure 3: Confusion matrix of BERT ensemble model for Abusive language detection
3. Results
The performance of the proposed model is measured in terms of precision, recall, 𝐹1 − 𝑠𝑐𝑜𝑟𝑒,
accuracy, and ROC value. The results for all the submitted models for Task-A and Task-B
are listed in Table 1. For Task-A, the proposed Ensemble (SVM + LR + RF) model achieved a
weighted precision, recall, and 𝐹1 -score of 0.81, an accuracy of 0.81, and a ROC value of 0.90.
The confusion matrix for Ensemble (SVM + LR + RF) model can be seen in Figure 1. The DNN
Confusion matrix
450
400
Not-Abusive 0.87 0.13
350
300
True label
250
200
Abusive 0.26 0.74
150
100
e
ive
siv
us
bu
Ab
t-A
No
Predicted label
Figure 4: Confusion matrix of SVM classifier for Abusive language detection
Confusion matrix
3000
Not-Abusive 0.99 0.01 2500
2000
True label
1500
0.78 0.22 1000
Abusive
500
ive
ive
us
us
Ab
Ab
t-
No
Predicted label
Figure 5: Confusion matrix of ensemble of SVM, LR, and RF classifiers for Threatening language
detection
model achieved a weighted precision, recall, and 𝐹1 -score of 0.78, an accuracy of 0.78, and a
ROC value of 0.83. The confusion matrix for the DNN model can be seen in Figure 2. The
Ensemble (Three variants of BERT) model achieved a weighted precision, recall, and 𝐹1 -score
of 0.79, an accuracy of 0.79, and a ROC value of 0.85. The confusion matrix for the Ensemble
(Three variants of BERT) model can be seen in Figure 3. The SVM classifier achieved a weighted
precision of 0.81, recall of 0.80, 𝐹1 -score of 0.80, an accuracy of 0.80, and a ROC value of 0.89.
Confusion matrix
3000
0.96 0.04 2500
Not-Abusive
2000
True label
1500
0.74 0.26 1000
Abusive
500
e
ive
siv
us
bu
Ab
t-A
No
Predicted label
Figure 6: Confusion matrix of AdaBoost classifier for Threatening language detection
The confusion matrix for the SVM classifier can be seen in Figure 4.
For Task-B, the Ensemble (SVM + LR + RF) model achieved a weighted precision of 0.84,
recall of 0.85, 𝐹1 -score of 0.81, accuracy of 0.85, and a ROC value of 0.81. The confusion matrix
for Ensemble (SVM + LR + RF) model for threatening language identification can be seen in
Figure 5. The AdaBoost classifier achieved a weighted precision of 0.81, recall of 0.83, 𝐹1 -score
of 0.81, accuracy of 0.83, and a ROC of 0.74. The confusion matrix for the AdaBoost classifier
for Threatening language identification can be seen in Figure 6.
4. Conclusion
Urdu is widely used on social media and in the digital world. Users publish a disproportionate
number of abusive and threatening Urdu social media posts. Various ensemble-based models
based on machine learning and deep learning are proposed in this study. The suggested
Ensemble-based approach, which combines support vector machines, logistic regression, and
random forest classifiers, outperformed all other models in terms of identifying abusive and
threatening language in Urdu social media postings. For abusive language identification, the
proposed ensemble-based model achieved a weighted 𝐹 1-score of 0.81, accuracy of 0.81, and
ROC of 0.90, while for threatening language identification, it achieved a weighted 𝐹 1-score of
0.81, accuracy of 0.85, and ROC of 0.81.
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