## 4 Experiment and Analysis  
### Data Collection  
To assess model performance, a comprehensive analysis was conducted on the proposed method in executing zero-shot unsupervised detection of Malay offensive language. We collected a dataset of Malay offensive language from Twitter to support the training and evaluation of OLD models. The dataset was meticulously constructed to encompass tweets containing specific Malay offensive keywords, alongside contrasting tweets devoid of such keywords. This step aimed at creating a representative dataset covering various facets of offensive language. Initially, a specific set of Malay offensive keywords was identified, addressing diverse aspects of offensive discourse, including but not limited to personal attacks, racial discrimination, or malicious statements. Examples include terms like \"Bodoh\" (Stupid/Foolish), \"Mampus kau\" (You are dead), \"Lelaki jamban\" (Men are unpleasant as a toilet), \"Kafir\" (Arabic term often used in Islamic contexts to refer to a non-believer or someone who rejects or disbelieves in Islam), and \"Bapok\" (Used to insult or demean transgender individuals or effeminate gay men). Leveraging Twitter's API and its keyword filtering functionality, we collected tweets containing these offensive keywords. To construct the comparative dataset, we utilized the Twitter API to gather tweets that did not include offensive keywords. In the realm of cyberbullying, personal attacks emerge as its primary manifestation and a focal point in OLD tasks. Detecting discriminatory language proves challenging, relying on models to autonomously discover underlying patterns within the corpus. Subsequently, the collected tweets undergo data cleansing to eliminate duplicates, invalid information, and irrelevant content. Text preprocessing, encompassing tasks like tokenization and stop-word removal, enhances the efficacy of subsequent model training. The final dataset comprises approximately 625 offensive tweets and about 2200 non-offensive tweets. It is crucial to note that the presence of offensive words in a tweet does not inherently classify it as offensive. Manual scrutiny of each tweet, considering the evolving connotations of specific terms within niche online cultures, is imperative. Comments with explicit vulgarities are directly categorized as offensive. As for texts with strong malicious intent but lacking explicit offensive terms, their classification often relies on subjective human perception. As of now, there are no universally quantifiable rules, and the majority sentiment determines the standard. Three individuals participated in the dataset annotation process, adopting a one-person labeling and two-person verification approach to ensure consistency in judgment criteria. In cases of differing opinions, a voting mechanism was employed to determine the classification outcome. For the source language of model training, this paper utilizes the English annotated dataset from the 2019 OffensEval shared task 6, known as EN-OLID [61]. Previous efforts predominantly focused on detecting specific types of malicious content, neglecting to explore commonalities across different types of offenses (e.g., personal insults termed as cyberbullying and insults targeting groups termed as hate speech). This dataset takes a comprehensive approach, proposing the OLID (Offensive Language Identification Dataset) annotations. It employs a hierarchical modeling approach, discerning whether comments are malicious, identifying the type of malicious comments (non-targeted or targeted), and pinpointing the target of malicious comments (individuals or groups). These hierarchical annotations capture commonalities across diverse offensive text data, rendering it applicable to various OLD tasks. To comprehensively assess the effectiveness and generality of the proposed method in cross-language OLD task, experiments also employed Danish and Arabic datasets released in the 2020 OffensEval shared task 12 [62].