Social media platforms have connected billions of people and helped them share their views on these platforms. However, the problem arises when malicious users abuse, show anger, and threaten others on these platforms. Therefore it is indeed necessary to detect such hostile/harmful content. So far, several studies have been conducted for hostile and negative content detection, but most of the work revolves around English. Hence to facilitate research for low-resource languages such as Urdu, the organizers of the “EmoThreat: Emotions & Threat Detection in Urdu”shared task at FIRE 2022 have introduced two tasks for emotion classification and threatening language detection. In this paper, we investigate the performance of several transformer-based models and observe that the MBERT model performs the best for emotion classification. In contrast, the MURIL model performs the best for threatening tweet classification. Finally, our team hate-alert stands 3rd in task A, 2nd in subtask 1B and 2nd in subtask 2B.
Most of our population is connected to each other via the social network; the social network
has and is helping us get news, express our opinion, and slowly influence our growth as a
society. It has been seen that Facebook has roughly 2.93 billion monthly active users1, Instagram
has 1.21 billion monthly active users2, and Twitter has over 450 million monthly active users
globally3. Therefore it can understand the enormous amount of content being shared over the
Internet. One of the issues with these content-sharing platforms is that occasionally bad actors
share negative, abusive, threatening, and aggressive posts on this platform and endanger the
well-being of millions of people [
To mitigate the efect of malicious content, platforms like Facebook 4 and Twitter5 have
already made guidelines that the platform users must follow to keep these platforms healthy
and safe; besides, they hired moderators [
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to the large volume of content, it is dificult to filter all the content posted on the platforms
manually. So far, several studies have been conducted to detect such negative and hostile
content automatically [
Therefore to engage and facilitate the research around low resoruce languages, the organizers
of the “EmoThreat: Emotions & Threat Detection in Urdu [
In this paper, we investigate several transformer-based models for the classification task, which have already been seen to outperform the existing baselines and stand as a state-of-theart model for various tasks considering hateful and abusive speech [12, 13, 14]. We conduct pre-processing, data sampling, hyper-parameter tuning, etc., to construct the model. The best models stand 3rd in task A (Multi-label emotion classification in Urdu), 2nd in subtask 1B( Classify the given tweet as “threatening” and “non-threatening”), and 2nd in subtask 2B(If the tweet is classified as a “threatening” tweet, then it should be further classified as a “individual” or a “group” threat).
Due to the exponential growth of social media platforms, sharing content on these platforms has expanded tremendously, further increasing the malicious content on these platforms. Therefore detection of such malicious content has gained significant attraction among the research community.
In 2017, Waseem et al. [
In order to accomplish the classification objective of identifying hate/ofensive speech
embedded in Tweets, Davidson et al. [
Pitsilis et al. [15] examined recurrent neural networks (RNNs) in 2018 to detect the ofensive language in English. The author found that RNNs performed admirably on this task using ensemble methods, achieving an F1-score of 0.9320. RNNs preserve the outcomes of each
step the model conducts. This technique can capture linguistic context within a text which is essential for detection. While RNNs have been projected to do well with language models, other neural network models, including CNN and LSTM, have succeeded at identifying hate/ofensive speech [16, 17].
Transformer-based [18] language models, such as BERT and m-BERT [19], have recently
gained popularity in various downstream tasks, like categorization and span detection.
Transformerbased models have formerly been found to outperform [
The shared tasks present in this competition are divided into two parts. The datasets have been sampled from Twitter. The Task A is to perform multi-label emotion classification given Urdu Nastalíq tweets [20, 21, 22, 23]; it has to be classified into one or more of the following categories: Neutral, Happiness, Surprise, Sadness, Fear, Disgust, Anger. The task B [24, 25, 26, 27, 28] is further divided into two parts. In the first part(1B), the task is to classify a tweet as threatening or non-threatening; in the second part, the task is to classify threatening tweets into two categories: “group” or “individual” threats. The presented data has been collected and annotated from Natural Language and Text Processing Laboratory8 at Center for Computing Research9 of Instituto Politécnico Nacional, Mexico. 3.1. Task A 3.2. Task B This task is a multi-class classification task in which tweets need to be classified into seven classes, namely: Anger, Disgust, Fear, Sadness, Surprise, Happiness, Neutral. The training dataset has total 7,800 instances and the test dataset has total 1,950 instances. The dataset description for this task has been represented in Table 1.
This is a classification task of identifying/detecting threatening language in Urdu with two sub-tasks.
• Sub-task 1B : Binary classification of the tweets as threatening and non-threatening • Sub-task 2B : If the tweet is classified as a threatening tweet then it should be further classified as a ”group” or ”individual threat”.
For the task B, the training dataset is having 3,564 instances and the test dataset has 935 instances which is annotated as threatening(group / individual) and non-threatening. The dataset distribution is presented in Table 2. and Table 3
This section explains the transformer-based models that have been explored. For task A (Multilabel emotion classification), we experimented with MBERT [ 19] and MURIL [29] models10. For subtask 1B(Binary classification of threatening language), we experimented with the following models: MBERT, MURIL, “dehatebert-mono-arabic”11 [30] and “indic-abusive-allInOneMuRIL”12 [31]. The “dehatebert-mono-arabic” model is an MBERT variant, which is fine-tuned on the Arabic hate speech dataset, and the “indic-abusive-allInOne-MuRIL” model is a MURIL variant previously finetuned on eight diferent abusive Indic languages considering Urdu. For the sub-task 2B(fine-grained classification of threatening language), we only experimented with 10Code used from: https://github.com/hate-alert/IndicAbusive 11https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-arabic 12https://huggingface.co/Hate-speech-CNERG/indic-abusive-allInOne-MuRIL
MBERT and MURIL models13.
The Task A is a multi-label classification problem, where each post can be classified among one or more categories. As discussed above we fine tuned transformer-based MBERT and MURIL models and added a classifier layer on top of that. BCE loss function has been used for calculating the loss.
Subtasks 2A and 2B is a binary and ternary classification problems. Here we also add an extra classification layer on top of the transformer models we used. For this subtask, the CrossEntropy loss function has been used as a loss function. Also, as seen from table 3, we can observe that the data is imbalanced; therefore, appropriate weights have been added to the classes before fine-tuning the models.
The models have been run for 5 epochs with Adam optimizer[32] and initial learning rate of 2e-5. As no validation dataset was given, we divided the training data points into 85% and 15% split and used the 15% as a validation set. We predict the test set for the best validation performance.
The performance of the task A, the multi-label emotion classification has been shown in Table 4. We observe that between MBERT and MURIL models, the MBERT model performs the 13Code used from: https://www.kaggle.com/vpkprasanna/bert-model-with-0-845-accuracy best in terms of all the evaluation metrics(Acc:0.612, Weighted F1: 0.709, Macro F1:0.615). For the sub-task 1B, we observe the MURIL model perform the best(Acc: 0.716, F1:0.737, ROCAUC:0.729) in terms of all metrics and the “indic-abusive-allInOne-MuRIL” model perform the second best(Acc: 0.672, F1:0.706, ROC-AUC:0.674). One interesting observation is that although “dehatebert-mono-arabic” and “indic-abusive-allInOne-MuRIL” models are previously finetuned on hate speech and abusive speech dataset, further fine-tuning them with the threatening tweet dataset do not outperform the vanila MURIL model. For the sub-task 2B also we obseve the MURIL model perform the best(Acc: 0.535, F1:0.696, ROC-AUC:0.66).
To further understand when the model is failing, we manually inspected some misclassified tweets by the best-performing models. For the emotion classification task, we observed that the actual label itself is sometimes incorrect according to our judgment based on the translated tweets; therefore, the model is failing for such cases. For threatening tweet detection, sometimes the presence of words such as killing makes the prediction incorrect; the model cannot distinguish threatening and non-threatening tweets for such cases. We have shown the example of some misclassified tweets in Table 7 and 8.
In this shared task, we have experimented with several transformer-based models for multilabel emotion classification and threatening tweet detection. In specific, we explored MURIL, MBERT-based models. We observed that the MBERT model performed the best for the emotion classification, and for the threatening tweet classification, the MURIL model performed the best. Our team hate-alert stands 3rd in task A, 2nd in subtask 1B and 2nd in subtask 2B. [11] S. Butt, M. Amjad, F. Balouchzahi, N. Ashraf, R. Sharma, G. Sidorov, A. Gelbukh, EmoThreat@FIRE2022: Shared Track on Emotions and Threat Detection in Urdu, in: Forum for Information Retrieval Evaluation, FIRE 2022, Association for Computing Machinery, New York, NY, USA, 2022. [12] S. Banerjee, M. Sarkar, N. Agrawal, P. Saha, M. Das, Exploring transformer based models to identify hate speech and ofensive content in english and indo-aryan languages, arXiv preprint arXiv:2111.13974 (2021). [13] M. Das, S. Banerjee, P. 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