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License Attribution 4.0 International (CC BY 4.0). FIRE 2021: Forum for
Information Retrieval Evaluation, December 16–20, 2021, India

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# Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021

Maaz Amjad  Instituto Politécnico Nacional (IPN), Center for Computing
Research (CIC), Mexico  Alisa Zhila  Ronin Institute for Independent
Scholarship, United States  Grigori Sidorov  Andrey Labunets  Independent
Researcher, United States  Sabur Butt  Hamza Imam Amjad  Moscow Institute of
Physics and Technology, Russia  Oxana Vitman  Alexander Gelbukh

(2021)

###### Abstract

With the growth of social media platform influence, the effect of their misuse
becomes more and more impactful. The importance of automatic detection of
threatening and abusive language can not be overestimated. However, most of
the existing studies and state-of-the-art methods focus on English as the
target language, with limited work on low- and medium-resource languages. In
this paper, we present two shared tasks of abusive and threatening language
detection for the Urdu language that has more than 170 million speakers
worldwide. Both are posed as binary classification tasks where participating
systems are required to classify tweets in Urdu into two classes, namely: (i)
Abusive and Non-Abusive for the first task, (ii) Threatening and Non-
Threatening for the second. We present two manually annotated datasets
containing tweets labeled as: (i) Abusive and Non-Abusive, (ii) Threatening
and Non-Threatening. The abusive dataset contains 2400 annotated tweets in the
train part and 1100 annotated tweets in the test part. The threatening dataset
contains 6000 annotated tweets in the train part and 3950 annotated tweets in
the test part. We also provide logistic regression and BERT-based baseline
classifiers for both tasks. In this shared task, 21 teams from six countries
registered for participation (India, Pakistan, China, Malaysia, United Arab
Emirates, Taiwan), 10 teams submitted their runs for Subtask A —Abusive
Language Detection, 9 teams submitted their runs for Subtask B —Threatening
Language detection, and seven teams submitted their technical reports. The
best performing system achieved an F1-score value of 0.880 for Subtask A and
0.545 for Subtask B. For both subtasks, m-Bert based transformer model showed
the best performance.

###### keywords:

Natural language processing text classification Twitter tweets Urdu language
shared task abusive language detection threatening language detection

##  1 Introduction

In cyberspace, abusive and threatening content is a glaring problem that has
been present since the beginning of human interaction on the internet and will
continue to persist in future. Social media platforms today are the venues for
free expression for all communities, and community backlashes can result in a
lot of negative externalities. Thus, with the growth of social media platforms
and their audiences, regulating threatening and abusive content becomes a
concern for the welfare of all stakeholders. Though leading platforms such as
Twitter and Facebook have set up community standards for the prevention of
cybercrimes, early detection of such content is vital for the safety of
cyberspace.

Detection of abusive and threatening text is a complex problem as the
platforms find it challenging to maintain a balance between limiting the abuse
and giving users ample freedom to express themselves. Failing to meet the
balance can result in users losing trust in the platform as well as
disengagement with the content. Platforms also find it challenging to detect
texts in multiple languages, especially low resourced languages and code mixed
languages. Manual filtering and understanding of this content is logistically
daunting and resource unfriendly. It can also result in the delay of necessary
and timely action needed in case of active threats and abuse. Hence, Natural
Language Processing (NLP) researchers have been working on the early detection
of threats and abuse by providing various automated solutions based on machine
learning and deep learning in particular.

Several studies previously have attempted to deal with the problem of abusive
language [1, 2] and threat detection [3, 4]. These problems have been
attempted through supervised machine learning [5, 6, 7] and deep learning [8,
9, 10] approaches breaking it down into binary, multi-label, or multi-class
classification problems. However, these attempts are only limited to European
languages, Arabic, and a few South Asian languages such as Hindi, Bengali, and
Indonesian.

Here we present a new shared task for abusive and threatening language
detection in tweets written in Urdu. The task is aimed at driving attention
and effort of the research community to developing more efficient methods and
approaches for this vastly spoken language and highlighting difficulties that
specific to the writing system and the use of Urdu. The paper describes the
abusive and threatening language tracks
111https://www.urduthreat2021.cicling.org organized by the authors within the
Hate Speech and Offensive Content Identification (HASOC) evaluation tracks of
the 13th meeting of Forum for Information Retrieval Evaluation (FIRE) 2021
222http://fire.irsi.res.in/fire/2021/hasoc and co-hosted by Open Data Science
(ODS) Summer of Code initiative 2021 333https://ods.ai/competitions. The task
is comprised of two sub-tasks:

  1. 1.

Sub-task A: Abusive language detection 444https://ods.ai/competitions/urdu-
hack-soc2021. The task offered a dataset of Twitter posts ("tweets") in Urdu
language split into the training part with the annotations available to
participants and the testing part provided without annotations. The dataset
annotation procedure followed Twitter’s definition of abusive language
555https://help.twitter.com/en/rules-and-policies/abusive-behavior to identify
posts that are abusive towards a community, group, or an individual as the
ones meant to harass, intimidate or silence someone else’s voice. The tweets
were annotated in a binary manner, i.e., abusive or non-abusive. The
participants were asked to determine the correct labels for the testing part
and submit their annotations. The solutions were evaluated using F1 and ROC-
AUC metrics.

  2. 2.

Sub-task B: Threatening language detection
666https://ods.ai/competitions/urdu-hack-soc2021-threat. Similarly, the task
offered a dataset of tweets in Urdu annotated as threatening or non-
threatening split into the training and testing parts, with the annotations of
the testing part hidden from the participants. The annotation procedure for
the Sub-task B dataset followed Twitter’s definition of threatening tweets
777https://help.twitter.com/en/rules-and-policies/violent-threats-
glorification as those that are against an individual or group meant to
threaten with violent acts, to kill, inflict serious physical harm, to
intimidate, or to use violent language. The task and the evaluation procedure
were identical to Sub-task A

With these shared tasks our contributions are:

  * •

spreading awareness and motivating the community to propose more efficient
methods for automated detection of abusive and threatening messages in social
media in Urdu as well as providing means for standardized comparison as
emphasized in Section 2;

  * •

collection and annotation of the largest so far datasets for abuse and threat
detection in the Urdu language described in Section 4 , in particular, 3500
tweets annotated as abusive or not and 9,9500 tweets annotated as threatening
or not;

  * •

the train and test split that allows for a fair result comparison (see Section
5 for details and grounds) not only among the current participants but also
for future research;

  * •

provision of highly competitive baseline classifiers in Section 7;

  * •

overview and comparison of the submitted solutions for abusive language and
threat detection in Urdu in Sections 8 and 9.

##  2 Importance of Identifying Abuse and Threat in Urdu

Urdu is one of the largest spoken languages in South Asia. It is the national
language of Pakistan and has its roots in Persian and Arabic bearing
additional structural similarities with many languages from other language
families, e.g., Hindi [11, 12]. Urdu is spoken by more than 170 million
888https://www.ethnologue.com/language/urd people worldwide and the number is
increasing every day. Yet it lacks solutions and resources for the most
essential natural language processing problems.

Urdu is mostly written using the Nastalíq script. However, certain populations
also use the Devanagari script which is normally used for writing Hindi.
Hence, Urdu texts experience the phenomenon of digraphia that is the use of
more than one writing system for the same language. Additionally, Urdu is
quite complex linguistically [13] as its morphological and syntactic structure
is a combination of Turkish, Arabic, Persian, Sanskrit, and English. Hence,
contributing to Urdu is also fundamental for the success of other languages.

Population of the Urdu speaking countries have substantial access to social
media, and millions of speakers are exposed to unregulated or poorly regulated
hate, abuse, and threats. Various extremist and terrorist groups have
developed communities on social media platforms that spread abuse, threats,
and terror [14]. As they post in local languages, in particular, in Urdu, much
of the content is left unchecked until reviewed and reported manually.
Pakistan suffered decades of terrorism and had to resort to banning social
media on several occasions to tackle terrorism [15]. Hence, the development of
resources in Urdu for threat and abuse detection is an urgent requirement for
the safety of millions.

##  3 Literature Review

Offensive content encompasses a variety of phenomena including aggression [16,
17], sexism [18], hate speech [19, 5], threat detection [4, 9536729], toxic
comment detection [20], abusive language detection [1, 2] and many others.
Previous research [21] have attempted to distinguish various types of abuse
such as implicit vs. explicit abuse or identity vs. person-directed abuse to
identify more nuanced expressions of abuse.

Multiple annotated datasets are available for a variety of offensive content
phenomena sourced from numerous social media platforms and portals. Yahoo
Finance corpus [19] comprises English language texts from the Yahoo’s finance
portal which is annotated into two classes: clean and hate speech. Research
[5] collected a dataset of Twitter posts in English and annotated them into
three classes as sexism, racism, or neither. Similarly, work [22] also
annotated tweets in English into three classes, yet different from work [5]:
hate speech, offensive language, and neither. On the contrast, study [23]
distinguished four offensive classes in their collection of Twitter posts in
English: hateful, spam, abusive, and neutral.

Youtube has been another source for data collection for abusive language in
English [20, 24] as well as in other languages, in particular, Arabic [2]. In
particular, the study by Ashraf et al. [4] is based on the YouTube comment and
replies collection introduced by Hammer et al. [24] with additional annotation
of its subset as whether a threat is directed towards a group or an
individual. Another study [25] collects a dataset of 2,304 YouTube comments
with 6,139 replies in English and annotates it in two ways: a binary
annotation for abusive language as well as a three class annotation for
topics: politics, religion, and other.

Attempts have been made to create threat and abuse detection models for
Bengali language [3]. Posts and comments have been collected from different
pages of Facebook. Threatening and abusive language was labeled as “YES” in
the dataset and the rest of the data which is not abusive was labeled as “NO”.
For more detailed analysis of the available datasets, we recommend these
studies [26, 1, 27].

Apart from the papers proposing a single solution, a number of shared tasks
have been organized to incentivize creation of multiple robust systems for
offensive phenomena detection in texts. Some of the popular shared tasks are
OffensEval [28, 29] with available datasets in Greek, English, Danish, Arabic,
Turkish, and English; GermEval 2018 [30] for texts in German; TRAC shared task
[31] for Hindi, English, and Bengali; SemEval-2019 [32] for hate speech
detection in English and Spanish; HASOC 2019 and 2020 [33, 34] for German,
English, Tamil, Malayalam, and Hindi.

Among the common approaches for offensive language detection, we observe
feature-based approaches with traditional ML classifiers. Works [6, 7, 5, 35,
33, 34, 32, 29, 28] use various combinations of features such as N-grams, Bag-
of-Words (BOW), Part-of-Speech (POS) tags, Term Frequency—Inverse Dense
Frequency (TF-IDF) representation, word2vec representation, sentiments, and
dependency parsing features provided as input to the traditional ML models
such as Support Vector Machines (SVM), Logistic Regression (LR), Random Forest
(RF), Decision Tree (DT), Naive Bayes (NB), etc.

Among the more efficient approaches for the task, we see boosting-based
ensembles as well as neural networks, in particular, deep NNs such as
transformers. For example, Ashraf et al. [25, 4] used n-gram and pre-trained
word embeddings in combination with traditional ML (LR, RF, SVM, NB, DT,
VotingClassifier, and the boosting-based ensemble AdaBoost) as well as Neural
Network based methods (MLP, 1D-CNN, LSTM, and Bi-LSTM) for the abusive
language detection and for the prediction of an individual- vs. group-targeted
threat correspondingly. While the BiLSTM approach achieved an F1 score of 85%,
the use of conversational context along with the linguistic features achieved
even higher F1 score of 91.96% using an ensemble AdaBoost classifier.

BiLSTM and Convolutional Neural Networks (CNN) were used to tackle abusive
language and hate speech detection in multiple other works. Studies employing
graph embeddings to learn graph representations from online texts [9],
paragraph2vec [19], and Recurrent Neural Networks (RNN) with attention [10],
RNN with Gated Recurrent Units (GRUs) [8] have also shown encouraging results.
The pre-trained transformer methods such as RoBERTa, BERT, ALBERT and GPT-2 to
detect hate speech detection can be seen achieving high accuracies [36, 37,
38]. A recent study [39] applied XLM, BERT and BETO models to achieve
promising results on similar tasks for hate speech detection.

While each offensive subcategory uses different definitions for annotation,
similar methods can be applied across the offensive content detection tasks.
All these techniques can be used to test the best combinations for detection
of abuse and threat in the Urdu language[40, 41] and our study opens vast
avenues for researchers to achieve this goal.

##  4 Datasets Collection and Annotation

###  4.1 Threatening and Abusive Datasets Collection

In the beginning, we created a dictionary of most used abusive and threatening
words in Urdu. We used those words as keywords on Twitter to mine tweets
containing more abusive and threatening words in Urdu, which we manually added
to our dictionary. The dictionary includes words that appeared even ones to
threat or abuse someone. This dictionary is publicly available for research
purposes 999https://github.com/MaazAmjad/Threatening_Dataset.Thus, we
collected a sufficient number of abusive and threatening seed words which were
further used to crawl tweets through the Twitter Developer Application
Programming Interface (API)
101010https://developer.twitter.com/en/docs/twitter-api/v1/tweets/search/api-
reference/get-search-tweets using Tweepy library. Thus, we gathered enough
words and phrases that are used to threat or abuse individuals. We collected
tweets containing any of these keywords from our dictionary for a 20 month
period from January 1st, 2018 to August 31st, 2019. At this time the general
elections being held in Pakistan in July 2018. Typically, during the election
season, people tend to be more expressive when supporting as well as opposing
political parties. In total we crawled 55,600 number of tweets containing the
seed words.

###  4.2 Threatening and Abusive Datasets Pre-processing

Since Urdu shared many common words in Persian, Turkish and Arabic, so when we
crawled tweets using our initially collected words, the Twitter APA also
crawled many non-Urdu tweets. Since this research was primary focused on Urdu
lanaguage, we discarded all the non-Urdu tweets manually. Thus, two different
datasets have been created: (i) abusive dataset
111111https://github.com/MaazAmjad/Urdu-abusive-detection-FIRE2021, containing
3,500 tweets, 1,750 of them are abusive and 1,750 of them are non-abusive (ii)
threatening dataset 121212https://github.com/MaazAmjad/Threatening_Dataset;
containing 9,950 tweets, 1,782 threatening tweets and remaining tweets are
non-threatening.

###  4.3 Threatening and Abusive Datasets Annotation

We defined guidelines to annotate abusive and threatening tweets.

To annotate the dataset the annotators have been recruited. All of them
satisfied the following criteria: (i) country of origin - Pakistan; (ii)
native speakers of Urdu; (iii) are familiar with Twitter; (iv) aged 20–35
years; (v) detached from any political party or organization; (vi) have prior
experience of annotating data; (vii) educational level was a masters degree or
above.

We computed Inter-Annotator Agreement (IAA) using Cohen’s Kappa coefficient
[39] as it is a statistic measure to check the reliability between two
annotators. We provided instructions with task definitions (which are
reproduced below) and examples. Hierarchical annotation schema was used and
the main dataset was divided into two different datasets to distinguish
between whether the language is threatening ot non-threatening, abusive or
non-abusive. We followed Twitter definition to describe abusive
131313https://help.twitter.com/en/rules-and-policies/abusive-behavior and
threatening 141414https://help.twitter.com/en/rules-and-
policies/glorification-of-violence comments towards an individual or groups to
harass, intimidate, or silence someone else’s voice.

##  5 Training and Testing Dataset Split

Due to the requirements extended by the competition conditions and in purpose
of fair evaluation of the participant’s submission, a slightly larger portion
of the datasets was withheld as corresponding testing parts than it would be
done under ‘normal’ data science operations. Namely, 40% of the data was
withheld for the Threatening Language task, and 32%, for the Abusive Language
task. This is done, first of all, to ensure that the testing set is non-
trivial and represents well the variety of possible lexical expressions for
both classes. Second, during the active period of the competition, the
participants could observe the scores only from the “public” part of the
testing set, whereas the scores on the “private” part of the testing set were
made public only after the end of the competition. The partitioning of the
test set into public and private is necessary to avoid pure guessing or
tampering with predictions. We ensured that each partition of the testing data
was large enough to compute a score that is sufficiently reflective of the
actual performance of a system. The details are presented in Table 1.

Table 1: Descriptive table of the train/test split for abusive and threatening language datasets. In parentheses the fraction of positive, i.e., abusive or threatening respectively, labels is indicated.  Dataset | train | test | testPUB | testPRIV
---|---|---|---|---
Abusive | 2400 (49.50%) | 1100 (48.81%) | 400 (48.50%) | 700 (47.29%)
Threat | 6000 (17.85%) | 3950 (18.20%) | 1000 (15.80%) | 2950 (19.02%)

To be clear, the participants were handed out the entire test set without true
labels. After a submission, the scores were shown only for the public
partition of the test set. As it can be observed from Tables 5 and 7, there
still was some amount of shake-up among the scores and corresponding ranks on
the public and private partitions.

Now that both the training and the testing sets along with their true labels
are available to the research community, a different approach to train/test
split may be possible. However, for a fair comparison with the competition
submissions and results provided in this paper, we suggest following the
original split.

##  6 Evaluation Metrics

The submitted systems were evaluated by comparing the labels predicted by the
participants’ classifiers to the hidden ground truth annotations. For
quantifying the classification performance, we computed the commonly used
evaluation metrics: F1 score and ROC-AUC score. F1 score serves as a better
metrics for unbalanced datasets than Accuracy and, therefore, accommodates our
settings. The ROC-AUC score gives an estimate of the overall quality of the
model at the various level of predicted confidence thresholds and serves as a
more holistic evaluator.

##  7 Baselines

For the competition, the organizers prepared three baseline systems: two of
them reflected different aspects of traditional ML approach involving Bag-of-
Words features and were meant to be lower boundary scoring baselines while the
third system was based on the recent deep learning approach involving fine-
tuning of the BERT model [36].

###  7.1 LogReg with Lexical Features

All data pre-processing steps and most of the modeling details are identical
for both subtasks, abusive and threat detection, if not explicitly indicated
otherwise.

First, all possible word unigrams and bigrams were extracted from the training
dataset using the popular NLTK151515https://www.nltk.org [42] software package
for NLP, v. 3.4.5, counting the numbers for n-gram occurrences in the dataset.
Further, the occurrence threshold of 3 was applied to unigrams corresponding
to the 75th-percentile of all encountered unigrams. In other words, we took
the top 25% of most frequently occurring unigrams as features. Similarly, the
95th-percentile occurrence threshold of 4 was applied to bigrams. We also
added 2 additional features to account for Out-Of-Vocabulary (OOV) unigrams
and bigrams correspondingly. Eventually, the feature set was comprised of the
top occurring unigram features, top occurring bigram features, and the two OOV
features. The statistics for each feature type by the subtask dataset and the
total number of features is provided in Table 2.

Table 2: Feature type statistics for each subtask for the first baseline solution. Subtask | unigrams | bigrams | OOV features | total features
---|---|---|---|---
Abusive | 1775 | 1212 | 2 | 2989
Threat | 3640 | 3413 | 2 | 7055

Further, each tweet instance was represented as a straightforward count of
feature occurrences in the tweet, all OOV n-grams counting towards
corresponding special OOV features. No normalization was done as all tweets
have approximately the same length.

Logistic regression was selected as the classifier algorithm for our
traditional ML baseline solutions. In the first system, we used the
implementation from scikit-learn 161616https://scikit-learn.org [43] v.
0.22.1, which is a popular software package that includes a number of ML
algorithms. The max_iter parameter was set to 1000 to make sure the training
converges.

For the Threat Subtask dataset, where the positive and negative classes are
imbalanced, we also set the class_weight parameter to balanced which ensured
automatic instance reweighing.

The code is available at the organizers’s GitHub repository
171717https://github.com/UrduFake/urdutask2021/.

The balanced baseline secured the 8th place on the Threat Subtask private
leaderboard with F1-score equal to 0.49186, ROC-AUC, to 0.76991. The
unbalanced version applied in the Abusive Subtask came 12th on the private
leaderboard scoring 0.78684 F1-score, 0.88295 ROC-AUC.

####  7.1.1 A version of LogReg with lexical features and TF-IDF count

We also submitted a variation of the Log-Reg based classifier with a few
technical as well as conceptual modifications. Instead of a simple n-gram
occurrence count, the TF-IDF vectorization approach was used for text
representation. For this, the TfidfVectorizer function from the scikit-learn
package was used. It is to note that the types of features were unigrams only.
The number of features was set as in the previous approach.

Another purely technical difference was that the LogReg classifier was
implemented as a “single node neural network” which is algorithmically and
equationally equivalent to logistic regression.

The implementation was done using the PyTorch framework
181818https://pytorch.org [44]. This training set-up converged much sooner,
with mere 30 epochs, or in terminology of traditional ML, iterations, for both
datasets. The optimal number of epochs was determined using a validation
dataset which was 10% of the corresponding training data.

For the threatening language detection dataset, similarly to the previous
approach, the dataset balancing was performed by applying
torch.nn.BCEWithLogitsLoss function.

These differences in approaches were reflected in the final score difference.
Interestingly, for the abusive language detection task, while this variant
showed slightly higher scoring (0.77008 F1-score for this version vs 0.72928
F1 for the above version, and 0.86674 vs 0.85286 ROC-AUC) and, hence, the rank
(11th vs. 13th) on the public leaderboard, it actually showed same scores on
the private leaderboard, 0.78684 F1 and 0.88295 ROC-AUC, to the extent of
decimal precision displayed, sharing the 12th and 13th ranks.

More notably, in the threatening language detection task, the results and
scores returned by the two versions, not only varied largely, but the score
difference of the systems actually flipped significantly between the private
and public leaderboards. On the public leaderboard, the scikit-learn version
gained higher scores: 0.46471 F1 vs. 0.45161 F1 for the PyTorch version, and
0.79502 ROC-AUC vs. 0.78899 ROC-AUC for the PyTorch version. Yet on the
private leaderboard the scikit-learn version gained less: 0.49186 F1 vs.
0.51404 F1 for the PyTorch version, and 0.76991 ROC-AUC vs. 0.78212 ROC-AUC,
respectively.

This brings us to a likely conclusion that, no matter the ML package, for the
abusive task, the LogReg classifier along with the lexical bag-of-word
features is a sufficiently powerful tool that can properly converge on the
provided dataset learning a coherent pattern.

However, the threat detection task is a more complex task not only due to the
label imbalance but also due to the intrinsic semantic complexity of the
phrases, the latter having a much larger effect. Therefore, simple classifiers
and purely lexical features are too weak to capture higher levels of semantic
complexities and should not be relied on for this subtask.

###  7.2 BERT-based baseline

The dataset sizes of 2400 and 6000 items along with training example length
below 200 characters made the tasks approachable with transfer learning-based
methods using foundational deep BERT-like models.

The proposed deep learning-based solutions for both subtasks, Abusive and
Threat detection, used pretrained multilingual uncased BERT
191919https://huggingface.co/bert-base-multilingual-uncased [36] from
huggingface transformers library [45] as a base model.

Huggingface “built-in” BertForSequenceClassification
202020https://github.com/huggingface/transformers/blob/27d4639779d2d316a7c5f18d22f22d2565b84e5e/src/transformers/models/bert/modeling_bert.py#L1486
class with 2 output units was selected as a classification head, where pooled
output from [CLS] token is passed through a dropout layer, followed by a
linear layer with output units leading into cross-entropy loss function.

For the Abusive Subtask, we split the provided training dataset into TRAIN/DEV
sets via a standard 80:20 ratio. Using the TRAIN set, the model is further
fine-tuned for the target classification task for 3 epochs with minibatch size
of 32 and 60 minibatches per epoch. The total number of minibatches, and
correspondingly optimization steps, was 180. The fine-tuning process used the
DEV set to evaluate the model performance every 4 minibatches in order to load
a model with the best F1 score from checkpoints at the end of the fine-tuning.

For the Threat Subtask, we split the provided training dataset into TRAIN/DEV
sets via a 85:15 ratio. We deviated from the standard 80:20 split to let the
model train with slightly more data and more negative examples as a result at
the cost of less accurate F1 score. Using the TRAIN set, the model was later
fine-tuned for the target task for 5 epochs with minibatch size of 32 and 160
minibatches per epoch (total number of minibatches / optimization steps was
800). In our set-up, the model for the Threat Subtask converged slower than
the one for the Abusive subtask, therefore we trained the network for 5 epochs
instead of 3. The cross-entropy loss function additionally used inverse class
sizes as weights to account for imbalance. The fine-tuning used the DEV set to
evaluate the model every 8 minibatches (not 4 due to longer training) in order
to load a model with the best F1 score from checkpoints at the end of the
fine-tuning.

The first baseline model for the Abusive Subtask came 3rd on the private
leaderboard with F1-score equal to 0.86221, ROC-AUC to 0.92194. The second
baseline model for the Threatening Subtask came 9th on the private leaderboard
scoring 0.48567 F1-score, 0.70047 ROC-AUC. Considering the original BERT’s
[36] scores at GLUE and other benchmarks, as well as further progress in
language model pretraining [46], the first model’s relatively high F1 score
was expected. The Abusive Subtask was a sentence classification task with
little specific constraints (such as overly large sequence length or similar
obstacles), where deep bidirectional architecture-based and other large
pretrained language models generally outperform traditional machine learning
approaches in a number of domains. At the same time, better handling of class
imbalance in the Threat Subtask could help the second baseline model achieve
better convergence and a higher F1 score. We speculate, that domain-specific
improvements at preprocessing, additional intermediate-task training, and
complementary handcrafted features used along with the sentence embeddings can
further boost the score for both models. In other words, subject matter
knowledge of language and relevant threat landscape is indispensable for real-
world threat and abuse detection in Urdu language. Finally, we see
incorporating continued training and more domain-specific research in
adversarial training, out-of-distribution detection, and outlier detection as
viable directions to make a model robust to adversarial examples and
distribution shifts when it is deployed.

The code for this baseline is available on organizers’ GitHub
repository212121https://github.com/UrduFake/urdutask2021/blob/main/bert.

##  8 Overview of Submitted Solutions

This section gives a brief overview of the systems submitted to this
competition. 21 teams registered for participation, 10 teams submitted their
runs for Subtask A —Abusive Language Detection, 9 teams submitted their runs
for Subtask B —Threatening Language detection. Registered participants were
from different countries: India, Pakistan, China, Malaysia, United Arab
Emirates, Taiwan. This wide range of the regions where the interested
participants were located confirms the importance of the task. The team
members came from various types of organizations: universities, research
centers, and industry.

Table 3: Approaches used by the participating systems for Subtask A: Abusive
language detection

System/Team Name | Feature Type | Feature Weighting Scheme | Classifying algorithm | NN-based
---|---|---|---|---
hate-alert | laser embedding | $N/A$ | multi-lingual BERT | Yes
SAKSHI SAKSHI | contextual embedding | $N/A$ | Urduhack, BERT, and XLM-RoBERTa | Yes
Muhammad Hamayoun | word ${1,2}$-grams | frequency | SVM (sigmoid kernel) | No
Alt-Ed | BoW | TF-IDF | LogReg | No
Abhinav Kumar | char ${1,6}$-grams | TF-IDF | ensemble SVM+LogReg+RF | No
SSNCSE_NLP (late subm.) | char 10-gram | TF-IDF | MLP | Yes

###  8.1 Approaches to Text Representation

Participants used a variety of text representation techniques for tweet
representation. Team SAKSHI SAKSHI represented tweets using contextual
embedding representations that were obtained from training on an Urdu news
corpus. Individual participant Muhammad Hamayoun used traditional bag-of-words
representation for Subtask A and word2vec for word n-grams, $n=1,2$, for
Subtask B. The hate-alert team used pre-trained Urdu laser embeddings and
multi-lingual BERT222222https://huggingface.co/bert-base-multilingual-cased
pre-trained embeddings generated from an Arabic dataset. Team Alt-Ed used TF-
IDF text representation. Participant Abhinav Kumar used ${1,6}$-gram character
level TF-IDF features for tweet representation. A summary of approaches is
presented in Tables 3, 4.

Table 4: Approaches used by the participating systems for Subtask B:
Threatening language detection

System/Team Name | Feature Type | Feature Weighting Scheme | Classifying algorithm | NN-based
---|---|---|---|---
hate-alert | laser embedding | $N/A$ | multi-lingual BERT | Yes
SAKSHI SAKSHI | contextual embedding | $N/A$ | RoBERTa | Yes
Muhammad Hamayoun | word2vec embedding | $N/A$ | SVM (poly d=3) | No
Abhinav Kumar | char ${1,6}$-grams | TF-IDF | ensemble SVM+LogReg+RF | No
SSNCSE_NLP* | char 10-gram | TF-IDF | MLP | Yes

###  8.2 Classification Methods

To implement their classifiers, some participating teams used the traditional,
i.e., non-neural network based machine learning algorithms, while other teams’
submissions were based on various neural network architectures.

For Subtask B, team SAKSHI SAKSHI fine-tuned a pre-trained RoBERTa model from
the popular HuggingFace
library232323https://huggingface.co/transformers/model_doc/roberta.html on the
Urdu news corpus in an unsupervised manner. The same team used three
transformer-based techniques for Subtask A: (i) Urduhack, (ii) BERT, and (iii)
XLM-Roberta. Team hate-alert used Hate-speech-CNERG/dehatebert-mono-
arabi242424https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-arabic
model which is preliminary fine-tuned on an Arabic hate speech dataset.
Another participant, Muhammad Humayoun, used SVM with sigmoid kernel for
Subtask A and SVM with polynomial kernel of degree 3 for Subtask B.
Participant Abhinav Kumar used an ensemble of ML models SVM + LogReg + RF for
both subtasks. Similarly to one of the organizers’ baseline systems, team Alt-
Ed used Logistic Regression for Subtask A, which turned out to be team’s best
classifier for the task.

A summary of approaches is presented in Tables 3, 4, and in
UrduThreat@FIRE2021 [47].

Table 5: Final results and ranking for Subtask A: Abusive language detection | Private Leaderboard | Public Leaderboard
---|---|---
Team Names | Rank | F1 Score | ROC AUC | F1 Score | ROC AUC |
hate-alert | 1 | 0.880 | 0.924 | 0.853 | 0.920 |
SAKSHI SAKSHI | 2 | 0.868 | 0.935 | 0.839 | 0.934 |
Org’s BERT-based sol., out-of-comp. | $N/A$ | 0.862 | 0.921 | 0.846 | 0.923 |
SATLab | 3 | 0.856 | 0.917 | 0.827 | 0.910 |
Jie Yi Xiang | 4 | 0.853 | 0.914 | 0.842 | 0.915 |
Vlad Balanda | 5 | 0.849 | 0.945 | 0.824 | 0.901 |
Muhammad Humayoun | 6 | 0.825 | 0.824 | 0.805 | 0.9820 |
Igor Shatalin | 7 | 0.821 | 0.207 | 0.797 | 0.184 |
Alt-Ed | 8 | 0.820 | 0.890 | 0.770 | 0.874 |
Abhinav Kumar | 9 | 0.808 | 0.907 | 0.808 | 0.907 |
ai | 10 | 0.791 | 0.908 | 0.743 | 0.875 |
Org’s LogReg v1 sol., out-of-comp. | $N/A$ | 0.786 | 0.882 | 0.730 | 0.853 |
Org’s LogReg v2 sol., out-of-comp. | $N/A$ | 0.786 | 0.882 | 0.770 | 0.866 |
SSNCSE_NLP, late subm. | $N/A$ | 0.771 | 0.757 | 0.689 | 0.699 |
Table 6: Approaches used by the participating teams

System/Team Name | Text Representation | Feature Weighting Scheme | Classifying Algorithm | is NN-based?
---|---|---|---|---
Nayel | tri-gram | TF-IDF | linear SVM with SGD | No
Abdullah-Khurem | Word2Vec, GloVe, fastText | TF-IDF | textCNN | Yes
Hammad-Khurem | BoW | count (?) | ensemble XGBoost+LightGBM+AdaBoost | No
Muhammad Homayoun | char ${2,6}$-gram | N/A | CNN | Yes
Snehaan Bhawal | transformer embeddings | N/A | MuRIL | Yes
MUCIC | word- & char ${1,2}$-grams | TF-IDF | ensemble linSVM+LR+MLP+XGB+RF | Yes (MLP) & No
SOA NLP | char ${1,3}$-grams | TF-IDF | DNN | Yes
Dinamore&Elyasafdi_SVC | char 3-grams | TF-IDF | SVM | No
MUCS | word fastText emb & char ${2,3}$-grams | TF-IDF for char-grams | ensemble MLP+AdaBoost+GraidentBoost+RF | Yes (MLP) & No
Iqra Ameer | transformer emb | N/A | BERT-base | Yes
Sakshi Kalra | transformer emb | N/A | RoBERTa-urdu-small | Yes

Table 7: Final results and ranking for Subtask B: Threatening language detection | Private Leaderboard | Public Leaderboard
---|---|---
Team Names | Rank | F1 Score | ROC AUC | F1 Score | ROC AUC |
hate-alert | 1 | 0.545 | 0.810 | 0.489 | 0.798 |
SATLab | 2 | 0.545 | 0.815 | 0.548 | 0.814 |
Somnath Banerjee | 3 | 0.518 | 0.791 | 0.475 | 0.776 |
Org’s LogReg v2 sol., out-of-comp. | $N/A$ | 0.514 | 0.782 | 0.451 | 0.788 |
Muhammad Humayoun | 4 | 0.501 | 0.704 | 0.461 | 0.698 |
Jie Yi Xiang | 5 | 0.494 | 0.770 | 0.463 | 0.771 |
SAKSHI SAKSHI | 6 | 0.492 | 0.781 | 0.534 | 0.819 |
Org’s LogReg v1 sol., out-of-comp. | $N/A$ | 0.491 | 0.769 | 0.464 | 0.795 |
Org’s BERT-based sol., out-of-comp. | $N/A$ | 0.485 | 0.700 | 0.519 | 0.765 |
Vlad Balanda | 7 | 0.478 | 0.799 | 0.444 | 0.786 |
Abhinav Kumar | 8 | 0.349 | 0.735 | 0.425 | 0.745 |
Owais Raza | 9 | 0.551 | 0.757 | 0.129 | 0.740 |
SSNCSE_NLP, late subm. | $N/A$ | 0.805 | 0.657 | 0.825 | 0.661 |

##  9 Results and Discussion

Table 5 presents results and ranking for Abusive Language detection subask.
Table 7 presents results and ranking for Threatening Language detection
subtask. The systems are ranked by their F1 score on the private leaderboard.

We observe that except for one participant system, all the other participating
teams’ systems outperformed the proposed LogReg baselines in terms of F1 score
for Subtask A. However, only two systems, hate-alert’s and SHAKSHI SHAKSHI’s,
outperformed the proposed BERT-based baseline. For Subtask B, on the contrast,
quite a few systems scored below the described LogReg baseline solutions.
Interestingly, even the organizers’ BERT-based solution did not achieve higher
scores than the LogReg baselines despite that the size of the training dataset
for Subtask B was larger than the one for Subtask A. Eventually, only the top
3 systems, hate-alert, SATLab, and participant Somnath Banerjee, achieved
higher F1 scores than the organizers’ Keras-based implementation of Logistic
Regression described in Section 7.1.1. Interestingly, although the two LogReg-
based baselines score closely on Subtask A, their scores differ substantially
for Subtask B. It might be due to the different values of the number of
iterations parameter that permitted the LogReg-v2 system to converge on the
larger training set in Subtask B, while in Subtask A LogReg convergence
arrives sooner, partly due to a smaller training set size.

Among all the submitted runs for both sub-tasks, the hate-alert team’s
solution achieved the best F1 score and ranked highest. Their solutions are
based on mBERT dehatebert-mono-arabic252525https://huggingface.co/Hate-speech-
CNERG/dehatebert-mono-arabic model that is trained on an Arabic news corpus.
It is plausible that the combination of a powerful deep learning model and
fine-tuning on a relevant, although somewhat unexpectedly, dataset was key for
the high performance. These results may open a way to further research about
the effect of direct knowledge transfer among languages that use the same
script, in particular, Nastalíq.

Table 8: Aggregated performance statistics for Subtask A: Abusive language detection. Numbers after a slash include organizers’ solutions. | Private Leaderboard | Public Leaderboard
---|---|---
Agg. Stats. and Percentiles | F1 Score | ROC AUC | F1 Score | ROC AUC
mean | 0.831/0.827 | 0.830/0.844 | 0.800/0.796 | 0.827/0.839
std | 0.033/0.035 | 0.214/0.190 | 0.049/0.049 | 0.225/0.199
min | 0.771/0.771 | 0.207/0.207 | 0.689/0.689 | 0.184/0.184
10% | 0.791/0.786 | 0.757/0.777 | 0.743/0.734 | 0.699/0.745
25% | 0.814/0.795 | 0.857/0.882 | 0.784/0.770 | 0.875/0.868
50% | 0.825/0.823 | 0.908/0.907 | 0.808/0.806 | 0.907/0.904
75% | 0.855/0.855 | 0.921/0.920 | 0.833/0.836 | 0.917/0.919
90% | 0.868/0.866 | 0.935/0.932 | 0.842/0.845 | 0.934/0.931
max | 0.880/0.880 | 0.945/0.945 | 0.853/0.853 | 0.982/0.982

Overall for Subtask A, 75% of the participating systems obtained F1 score
higher than 0.814 as it can be observed from the 25th percentile in Table 8.
This is a good indicator that the task of abuse detection for tweets in Urdu
can be achieved by automated means. In Table 5 we also observe that most of
the top performing systems achieve both better F1 score and better ROC AUC for
Subtask A.

In contrast, the task of threat detection for tweets in Urdu turned out to be
extremely challenging as more than 90% of the systems could not pass the 0.8
F1 score bar as may be observed in Table 9. Nevertheless, the top performing
system SSNCSE_NLP achieving F1 score of 0.805 (Table 7) provides a promising
perspective that this task is also solvable with the current methods and means
of NLP available for the Urdu language.

However, at this moment it is still too soon to judge whether any of these
approaches are ready to be applied “in the wild”. While the results of over
0.88 F1 score shown by the winning hate-alert system on Subtask A are
impressively high, the modest size of the provided training and testing
datasets cannot guarantee the same performance on an arbitrary text input. To
ensure the robustness of the presented approaches, more multifaceted research
at a larger scale is needed. We see that one of the paths is a community-
driven effort towards the increase of available resources and datasets in the
Urdu language.

Table 9: Aggregated performance statistics for Subtask B: Threatening language detection. Numbers after a slash include organizers’ solutions. | Private Leaderboard | Public Leaderboard
---|---|---
Agg. Stats. and Percentiles | F1 Score | ROC AUC | F1 Score | ROC AUC
mean | 0.528/0.521 | 0.762/0.759 | 0.479/0.479 | 0.761/0.766
std | 0.113/0.099 | 0.050/0.048 | 0.168/0.146 | 0.051/0.045
min | 0.349/0.349 | 0.657/0.657 | 0.129/0.129 | 0.661/0.661
10% | 0.465/0.479 | 0.699/0.701 | 0.395/0.429 | 0.694/0.706
25% | 0.492/0.491 | 0.741/0.735 | 0.448/0.451 | 0.741/0.745
50% | 0.510/0.501 | 0.776/0.770 | 0.469/0.464 | 0.774/0.776
75% | 0.545/0.545 | 0.797/0.791 | 0.523/0.519 | 0.795/0.795
90% | 0.576/0.550 | 0.810/0.808 | 0.576/0.545 | 0.815/0.811
max | 0.805/0.805 | 0.815/0.815 | 0.825/0.825 | 0.819/0.819

##  10 Conclusion

This paper presents a shared task in identifying threatening and abusive
language in Urdu, namely, the CICLing 2021 track @ FIRE 2021 co-hosted with
ODS SoC 2021. For this track, the organizers collected two original datasets
of text tweets in Urdu, one annotated for abuse (Subtask A) and the other, for
threatening content (Subtask B). We also provided a training and testing split
for both datasets, with the ground truth labels hidden from the participants
for the testing parts of the datsets. The solutions were submitted in the form
of proposed annotations for the testing sets along with the confidence score
provided by the participants’ systems. The submitted annotations were compared
with the ground truth label to compute the F1 score, while the submitted
confidence scores served for ROC AUC metric computation. The solutions were
ranked by the achieved F1 scores.

In this shared task, twenty one team from six different countries registered
for the competition, and seven teams submitted their solutions. Participants
used different techniques ranging from the traditional feature-crafting and
application of traditional ML algorithms to word representation through pre-
trained embeddings to contextual representation and end-to-end transformer
based methods. An uncommon solution included an ensemble of traditional ML
classifiers, SVM+LogReg+RF, whereas the particularly successful solutions used
specialized BERT-based systems such as multi-lingual BERT and XLM-RoBERTa.

In the abuse detection subtask, team hate-alert outperformed all other systems
with m-BERT transformer model achieving F1 score of 0.880. This and the rest
of the top 3 results in Subtask A indicate that the specialized transformer
based models tend to perform better compared to the feature-based traditional
ML models.

In the threat detection subtask, the hate-alert team was also a leader during
the official part of the competition with the 0.545 F1 score achieved by the
same m-BERT system. However, the results submitted by team SSNCSE_NLP after
the official part of the competition was closed showed a much higher F1 score
of 0.805. We advert that after the end of the official part of the
competition, the ground truth annotations for the testing sets were revealed
to public, by this potentially putting the late submitting teams into a more
advantageous position compared to the official track participants. Therefore,
late submissions were not assigned a rank. Additionally, the technical details
of SSNCSE_NLP’s solution should be enquired from the corresponding team.

This shared task aims to attract and encourage researchers working in
different NLP domains to address the threatening and abusive language
detection problem and help to mitigate the proliferation of offensive content
on the web. Moreover, this track offers a unique opportunity to fully explore
the sufficiency of textual content modality and effectiveness of fusion
methods. And last but not least, the annotated datasets in Urdu are provided
to the public to encourage further research and improvement of automatic
detection of threatening and abusive texts in Urdu.

###### Acknowledgements.

This competition was organized with the support from the Mexican Government
through the grant A1-S- 47854 of the CONACYT, Mexico and grants 20211784,
20211884, and 20211178 of the Secretaría de Investigación y Posgrado of the
Instituto Politécnico Nacional, Mexico.

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