## 3 Proposed Methodology
### Baselines  
It has been widely accepted that vulgar remark detection can be performed with sufficient accuracy using a basic keyword-matching method to find specific words or phrases of interest within a text [49]. By comparing the input text to a predetermined list of keywords, such a method searches for exact matches with a predetermined lexicon or a list of vulgar keywords [50]. Often, the input text is also preprocessed to remove any unnecessary words or information before keyword matching. Tokenization and removal of stop words or punctuation are a few potential strategies. Numerous modifications of the keyword-matching method have been applied, including regular expressions [51], string matching algorithms [52], or pattern matching [53; 54], all of which can be used to carry out the matching process. The algorithm analyzes each keyword in the text to determine whether it is used alone or as a component of a longer phrase. Keyword matching has the advantage of being efficient, straightforward, and explainable [55]. Because the input text and keyword list are directly compared, the computational overhead is typically very low. The algorithm can handle a large number of keywords, making it suitable for tasks that call for the simultaneous identification of multiple specific terms. However, one serious problem is that keyword lists are static, while the language on the Internet is constantly evolving. Therefore, the algorithm may have issues with newly emerging terms or terms that depend on context and are not on the keyword list. The keyword list, therefore, must be periodically updated if the method is to continue working effectively over time. Despite these shortcomings, keyword matching is still a useful technique in many applications. It provides an efficient and flexible way to pick specific words or phrases of interest. When combined with other methods like machine learning, keyword matching can be a useful component of systems that perform more thorough content filtering [56]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Annotator Pairs** & **Cohen’s Kappa** \\ \hline
1 and 2 & 0.92 \\ \hline
1 and 3 & 0.90 \\ \hline
2 and 3 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cohen’s Kappa score. \begin{table}
\begin{tabular}{c c} \hline \hline
**Annotator Pairs** & **Krippendorff’s Alpha** \\ \hline
1 and 2 & 0.927 \\ \hline
1 and 3 & 0.898 \\ \hline
2 and 3 & 0.917 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Krippendorff’s alpha. To perform keyword matching, one first needs to prepare the list or lexicon of such keywords: in this case, a lexicon of vulgar words and phrases. Although a variety of approaches can be designed for keyword extraction, such as rule-based linguistic approaches, statistical approaches, or even machine learning-based approaches, we can specify three general methods for extracting keywords or key phrases from text [55]. Namely, the required keywords can be extracted manually by a trained human designer or annotator, which assures high accuracy but is time-consuming and requires significant human effort. Additionally, the task of extracting vulgar words also poses a burden to the mental health of such human annotators. Secondly, the keyword list can be extracted fully automatically, which takes away all of the burden from the annotators. However, it is usually difficult to assure high accuracy of automatic extraction, since such methods rely on statistical properties of text (term occurrences, term frequencies, frequencies in the whole document, etc.). A third way is to improve the automatic extraction method to the point where it is as close to human judgment as possible and leave the remaining correcting work to the human annotator. Consequently, in this paper firstly, we proposed a keyword-matching baseline method for vulgar remark detection, which we based on a vulgar keyword extraction.