## 3 Proposed Methodology
### Data Preprocessing  
Apart from the baselines, we also applied classic machine learning (ML) algorithms to classify vulgar remarks. However, the ML algorithms required a number of specific data preprocessing and feature engineering techniques to be applied before the classification. Regarding data preprocessing, it is important to consider that the dataset comprises SNS comments, which may contain a significant amount of irrelevant information for the analysis. To minimize the impact of such unwanted and redundant features, we have carried out the following data preprocessing steps. Figure 8 shows each preprocessing step. **Removing punctuation:** When punctuation is removed from a text, all quotation marks and other special characters are also removed. Examples of punctuation used to denote pauses, emphasis, and other grammatical functions in written language include periods, commas, question marks, exclamation points, hyphens, parentheses, quotation marks, and other non-alphanumeric characters. The text data used in this study include a variety of punctuation and special characters, some of which may not have a discernible effect on the meaning of a sentence. In earlier research, [PERSON] et al. [59] showed that removing punctuation can improve text classification, including automatic cyberbullying detection, especially when using traditional ML algorithms. As a result, we also adopted this strategy and removed all punctuation from the text under analysis. Therefore, we also followed this approach and eliminated all punctuation from the analyzed text. The list of punctuation we considered for removal includes the following characters:'\"#()*+,-./;;<=>? @[]^|, etc. **Removing emoji and emoticons:** Eliminating these graphical representations of emotions, expressions, or symbols from a text involves using emoji and emoticon removal. Emoticons and emoji are frequently used in written communication to convey emotions and reactions or to provide additional context in online communications. There are two ways to deal with this type of information: either completely removing them from the text or replacing them with corresponding text representations. While emojis have been shown to aid text classification in some cases [60], this research primarily focuses on testing the baseline performance of simple ML classifiers on the dataset. Hence, we opted not to consider emojis and emoticons during the classification process. **Removing English characters:** Despite the fact that in Bangladesh Bengali is the primary official language, it is common for people to incorporate English words into their speech. However, since we were only focused on the Chittagonian dialect for this task, we eliminated any English characters. We did this in order to make sure that our analysis of the Chittagonian language was precise and focused. **Removing English digits:** Upon thorough examination of the dataset samples, we observed the presence of digits and numbers that did not carry specific semantic meaning. In standard practice, named entity recognition (NER) [61] tools are employed to identify and categorize such numerical entities, such as phone numbers, percentages, and currencies. However, for the Chittagonian dialect, no NER tool is currently available. To address this limitation and facilitate the initial experiment, we opted to remove the digits and numbers from the dataset. Nevertheless, we acknowledge the importance of handling numerical entities accurately in future experiments. Therefore, our future plans include developing a dedicated NER tool specifically designed for Chittagonian. This tool will significantly enhance the processing capabilities and enable more effective handling of such cases in subsequent research and applications. **Removing stopwords:** Stopwords are frequently eliminated when text data are processed for tasks involving natural language processing. Stop words are frequent words in a language that do not influence the meaning of a sentence.