NLP _ CUET at BLP -2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach (2023)

Abstract: The amount of online textual content has increased significantly in recent years through social media posts, online chatting, web portals, and other digital platforms due to the significant increase in internet users and their unprompted access via digital devices. Unfortunately, the misappropriation of textual communication via the Internet has led to violence-inciting texts. Despite the availability of various forms of violence-inciting materials, text-based content is often used to carry out violent acts. Thus, developing a system to detect violence-inciting text has become vital. However, creating such a system in a low-resourced language like Bangla becomes challenging. Therefore, a shared task has been arranged to detect violence-inciting text in Bangla. This paper presents a hybrid approach (GAN+Bangla-ELECTRA) to classify violence-inciting text in Bangla into three classes: direct , passive , and non-violence . We investigated a variety of deep learning (CNN, BiLSTM, BiLSTM+Attention), machine learning (LR, DT, MNB, SVM, RF, SGD), transformers (BERT, ELECTRA), and GAN-based models to detect violence inciting text in Bangla. Evaluation results demonstrate that the GAN+Bangla-ELECTRA model gained the highest macro f_1 -score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.

## introduction violence-inciting text refers to textual content that promotes or glorifies acts of violence or harm towards individuals, groups, or entities, including hate speech and extremist ideologies. detecting such text is crucial for preventing harmful activity and maintaining safety on social media. social media's widespread use by diverse religious and cultural factions has led to weaponization, inciting hatred and causing communal violence, resulting in significant loss of life and destruction. this issue persists not only in a specific geographical re-gion but also globally, escalating the longstanding issue. this paper aims to classify various forms of communal violence to illuminate this complex phenomenon and contribute to its mitigation. violence has evolved with society's advancements, with physical and psychological abuse now predominantly occurring online and on social networks, even though it was once face-to-face @xcite . previous studies reveal that social media platforms incite political and religious violence, thereby threatening communal harmony and societal stability @xcite . social networks have become a virtual civilization where people share views, feelings, photos, videos, and blogs. however, there is no defined mechanism for restricting violent content on these platforms @xcite . in recent years, tech giants like facebook, youtube, and twitter have been striving to achieve this goal @xcite . however, it is impossible to manually monitor these violent inciting contents that surf social media @xcite . therefore, developing such a system for detecting violenceinciting text is crucial to reducing illegal behavior and maintaining a clean information ecosystem. this work aims to build a system that can detect violence inciting text from bangla text concerning three different categories. this work's key contributions are illustrated in the following: ## related work while providing platforms for individual freedom of expression, social media and other blogging platforms can facilitate antisocial conduct, including hate speech, cyberbullying, and online harassment @xcite . several works has been conducted to detect aggressive comments @xcite , abusive comment, hate speech @xcite , trolling @xcite . however, few studies have been conducted to detect violence-inciting text. though several works have been done in high-resource languages, leaving low-resource languages like bangla out of the focus. to identify abusive language, eshan and hasan, 2017 utilized a dataset comprising 2.5k instances of abusive bangla text and evaluated the performance of several ml models (rf, nb, and svm) and achieved a maximum accuracy of 85% using svm with linear kernel and tri-gram features. kumar et al., 2018 categorized 15k english and hindi comments on aggression into overtly aggressive, covertly aggressive, and non-aggressive categories, expanding the corpus to include bangla aggressive comments (kumar et al., 2020). aroyehun and gelbukh, 2018 studied the effectiveness of dnn models in detecting aggression using enhanced data and pseudo-labeled samples. ishmam and sharmin, 2019 classified 5k bangla abusive facebook comments into six categories using a gru-based model, achieving 70.10% accuracy. the introduction of bert-based models significantly enhanced performance, surpassing all previous models on these datasets @xcite . @xcite presented a bangla aggressive text dataset, and later, they extended the previous dataset to create a new novel dataset named bad. they used a transformerbased ensemble technique to identify and categorize aggressive texts in bangla, achieving the highest weighted scores of 93.43% (coarse-grained) and 93.11% (fine-grained). as per our exploration, none of the past studies addressed classifying the violence-inciting texts in bangla. this work uses a hybrid approach incorporating gan and bangla-electra models to address the downstream task. ## task and dataset descriptions task organizers 1 created a gold standard corpus to detect violence-inciting language in social media. ## https://blp-workshop.github.io/sharedtasks to address this phenomenon, saha et al., 2023 developed a violence inciting text detection (vitd) corpus foot_0 in the bangla language. the task aims to implement a system that can detect offensive texts. the corpus consists of the text of three different classes: non-violence, passive violence, and direct violence. according to saha et al., 2023, the definition of each class is illustrated in the following: the dataset (vitd) accumulated 6046 texts from youtube comments in bangla. vitd is related to nine violent incidents during the previous 10 years. the task aims to quickly distinguish between violent threats to stop further incitement to violent acts. contribution to the identification and prevention of stimulation to violent acts online is the primary goal of this task. this work exploited several ml, dl, and transformer-based models to address the task. after investigating all models' performance, this work proposes a hybrid method using gan and bangla-electra to detect and classify violence-inciting bangla texts. we used the 'scikit-learn' and tensor-flow library to build ml and dl models. figure 2 shows an abstract view of the proposed system. first, the unwanted characters (urls, punctuation, and whitespace) are removed from the texts. we apply different feature extraction techniques (i.e., tf-idf, word2vec) to extract the textual features. this work employed six traditional ml models, such as logistic regression (lr), decision tree (dt), support vector machine (svm), multinomial naive bayes (mnb), random forest (rf), and stochastic gradient descent (sgd). we also used three dl methods, such as cnn, bilstm, and bilstm, with attention. this work employed four transformers fetched from huggingface foot_1 library. we built the transformer models with pytorch library to tackle the task, such as bangla-electra (bhattacharjee et al., 2021), bangla-bert-1 (sarker, 2020), bangla-bert @xcite , and bangla-bert-2 (kowsher et al., 2022). ## results ## conclusion this work addresses the challenge of fine-grained classification of texts inciting violence in bangla. we developed a solution by leveraging a benchmark dataset known as vitd. in this paper, we systematically investigated and compared 17 different baseline models, spanning various machine learning (ml), deep learning (dl), transformer, and generative adversarial network (gan) architec-tures. the experimentation revealed that integrating gans with transformers resulted in improved task performance. specifically, the combination of gan and bangla-electra demonstrated the highest macro f1-score (74.59) among all the models we employed, surpassing their performance. we intend to enhance our solution by leveraging ensemble techniques in future research endeavors. additionally, we will delve into the impact of resampling strategies on model performance, mainly as our dataset exhibits imbalance issues.