Team Error Point at BLP -2023 Task 1: A Comprehensive Approach for Violence Inciting Text Detection using Deep Learning and Traditional Machine Learning Algorithm (2023)

Abstract: In the modern digital landscape, social media platforms have the dual role of fostering unprecedented connectivity and harboring a dark underbelly in the form of widespread violence-inciting content. Pioneering research in Bengali social media aims to provide a groundbreaking solution to this issue. This study thoroughly investigates violence-inciting text classification using a diverse range of machine learning and deep learning models, offering insights into content moderation and strategies for enhancing online safety. Situated at the intersection of technology and social responsibility, the aim is to empower platforms and communities to combat online violence. By providing insights into model selection and methodology, this work makes a significant contribution to the ongoing dialogue about the challenges posed by the darker aspects of the digital era. Our system scored 31.913 and ranked 26 among the participants.

## introduction there is a great need for robust detection and classification algorithms in today's digital environment since violent incitement material is spreading so rapidly. this is especially essential for languages like bangla, where regional context and little changes in language play a large role in determining how violent content operates. the emnlp blp shared task on "violence inciting text detection" serves as a strong appeal to address this topic directly. one of our goals is to make a system that can handle the complicated language of bangla. this will make it easier and more accurate to find material that encourages violence.the idea for our study came from the important work of @xcite and the creation of the vio-lens dataset @xcite . the fundamental purpose of vitd is to detect and classify texts that contain components of incitement to violence. vio-lens, a unique annotated collection of over 10,000 bangla social media posts, marks a significant advancement in detecting and addressing violence-inciting language. with this resource, we aim to push the boundaries of threat assessment in bangla narratives, including those up to 600 words, seeking to not only identify evident risks but also redefine detection parameters. this research makes a valuable contribution to the wider effort to promote secure digital environments.several study subjects that have been discussed in the literature are locationindependent machine learning approaches for early fake news detection @xcite , combining audio and text elements to find violent incidents @xcite , and the creation of new methods like featurebased twitter sentiment analysis with enhanced denial handling @xcite . there is also an investigation into the possible use of a memristive lstm network for sentiment analysis @xcite . the method used in this study is based on the political security threat prediction framework, which is a mix of a lexicon-based approach and machine learning methods @xcite . additionally, the system has a racism detection model that leverages a stacked ensemble gcr-nn architecture @xcite . these initiatives demonstrate the applicability of mood analysis in several domains pertaining to security and social justice. to get further details on our research, refer to the publication titled "sentiment analysis of tweets using heterogeneous multi-layer network representation and embedding" @xcite . moreover, a significant advancement is shown in the mc-bert4hate model's ability to detect hate speech across many languages and translations @xcite . even though a lot of work has been made, these improvements also show how hard it is to understand bangla language. sometimes, traditional models have trouble understanding all the details in this language.our proposed methodology employs a diverse range of machine learning models to address the issues mentioned above. the algorithms included in this set are logistic regression, decision tree, random forest, multi-naive bayes, k-nearest neighbour (knn), support vector machine (svm), and stochastic gradient descent (sgd). this is in addition to using deep learning architectures like long short-term memory (lstm), convolutional neural network (cnn), and lstm-cnn hybrids to adapt to the unique features of the bangla spoken form. the inclusion of all individuals under this methodology facilitates the identification of and categorization of potential hazards, hence streamlining the process. 1 final implementation with an anonymous github link 2 .. ## literature review due to violence-inciting content, social media is both connecting and alarming. we found a new answer to this essential issue, giving hope. traditional machine learning and deep learning models classify violence-inciting literature in this study. this study built on natural language processing and hate speech identification research. the nlp survey on hate speech identification is useful for its problem formulations and methods @xcite . the transformative deep bidirectional transformer model bert by @xcite has changed natural language comprehension research. @xcite cyberbullying detection and classification work shows that online safety awareness has enhanced cyberbullying detection beyond hate speech. @xcite and @xcite participated in semeval-2019 task 6, which identified and categorized social media offensive language. @xcite from bnu-hkbu uic nlp team 2 employed a bert model to detect foul language, enriching this field. these studies show the importance of identifying and regulating offensive digital content. study social media bullying traces and their prognostic potential for online safety @xcite . the necessity of studying protected traits has helped burnap and williams improve twitter cyber hate detection @xcite . comment embeddings for hate speech identification advance the field and demonstrate their efficacy @xcite . mehdad and tetreault illuminated character-level abusive encounters, im-1 https://github.com/blp-workshop/blp_task1# leaderboard 2 https://anonymous.4open.science/r/emnlp_2023_ blp_workshop_task1-0fb2 proving our comprehension of abusive language @xcite . due to variances in methods and datasets, these research' results vary in accuracy despite their importance. this comprehensive review uses multiple methodologies and data augmentation to fill this critical gap in our knowledge. we want to improve bangla sentiment analysis and offensive language identification datasets and models. our research will illuminate content filtering and internet safety in underrepresented languages. ## data and methodology in this section, we present the data sources and preprocessing steps, along with the methodology encompassing machine learning and deep learning models. ## results and discussion in this section, we present the results of our experiments and engage in a comprehensive discussion of the findings. our study aimed to address the challenge of violence inciting text detection using a combination of machine learning and deep learning models. we used various algorithms and techniques to analyze and classify text data into different categories of violence, namely direct violence, passive violence, and non-violence. the machine learning models displayed varying degrees of performance in classifying violence inciting text in ## conclusion our research underscores the critical importance of detecting and classifying violent incitement text within the realm of natural language processing (nlp). drawing inspiration from the emnlp blp shared assignment on violence inciting text detection and building upon the foundational work, we aimed to redefine the parameters of danger assessment in the context of the bangla language. this study undertakes a comprehensive evaluation of machine learning and deep learning models to assess their effectiveness in categorizing literature that incites violence. conventional machine learning algorithms, such as logistic regression, decision tree, random forest, multi-naive bayes, knn, svm, and sgd, consistently demonstrate strong and reliable performance. notably, support vector machines (svm) and stochastic gradient descent (sgd) stand out for their efficacy in accurately classifying violent content. deep learning models, including long short-term memory (lstm), convolutional neural network (cnn), and the hybrid lstm-cnn, also exhibit significant capabilities. lstm, in particular, emerges as a standout performer among the deep learning models. this study's limitations include language and dataset specificity, data imbalance, model interpretability, and computational resource requirements. future research may encompass multilingual expansion, contextual analysis, user-level profiling, ethical considerations, human-in-the-loop approaches, cross-domain application, and realworld deployment of violence-inciting text detection models.