Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lalrinmawii, R.; Lalramhluna, Robert; Gunavathi, R. Title A name given to the resource Detecting Abusive Comments in Mizo: A Machine Learning Approach for a Low-Resource Language Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS;Issue;2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AVSS65446.2025.11149909" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AVSS65446.2025.11149909</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017422795?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017422795?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lalrinmawii R., Christ University, Department of Data Science, India; Lalramhluna R., National Institute of Technology, Department of Computer Science &amp; Engineering, India; Gunavathi R., Christ University, Department of Data Science, India Description An account of the resource The detection of abusive language in online spaces is crucial for ensuring a safe digital environment, particularly for low-resource languages like Mizo. Mizo, a tonal Tibeto-Burman language spoken primarily in Mizoram, India, poses significant computational challenges due to its phonetic complexity and limited linguistic resources. This research presents a method based on machine learning for abusive comment detection in Mizo, addressing the lack of annotated datasets and specialized NLP tools. A structured pipeline involving data collection, preprocessing, feature engineering, and model evaluation was implemented. Our study compares the effectiveness of several conventional machine learning methods, such as Random Forest, Support Vector Machines (SVM), Logistic Regression, and XGBoost, against transformer-based models such as Multilingual BERT(mBERT) and MizBERT. According to experimental data, MizBERT achieves the highest accuracy and F1-score, outperforming all other models by a substantial margin. This work contributes to the development of computational tools for Mizo NLP, laying a foundation for automated moderation systems and fostering digital inclusivity for Mizo-Speaking communities. 2025 IEEE. Subject The topic of the resource abusive comment detection; content moderation; digital safety; Logistic Regression; low-resource NLP; machine learning; MizBERT; Mizo language; multilingual BERT (mBERT); n-grams; Random Forest; Support Vector Machines (SVM); TF-IDF vectorization; tonal language processing; transformer models; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 26436213; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online