<![CDATA[This study aims to analyze trending topics related to the use of slang words on social media by utilizing natural language processing (NLP) techniques. The main focus of this research is to understand the patterns and trends of slang use on social media platforms, which can uncover important social and linguistic dynamics. The dataset used consisted of tweets in Indonesia and United Kingdom containing slang words, collected from Twitter over a six-month period. The analysis process begins with data cleansing to eliminate irrelevant elements, followed by tokenization and lemmatization to normalize the text. Furthermore, the Support Vector Machine (SVM) and Random Forest classification models are applied to detect and classify slang words in the dataset. The results show that the SVM model achieves a slang detection accuracy of 88% with an F1-score of 0.87, while the Random Forest model achieves an accuracy of 85% with an F1-score of 0.84. Further linguistic analysis showed that 60% of slang words are most commonly used in informal contexts such as everyday conversation, while the other 40% are related to popular culture trends, including music, movies, and fashion. In addition, these findings indicate that there is a variation in the use of slang between Indonesian and United Kingdom-speaking Twitter users, where slang in Indonesian tends to be more creative and contextual, while in United Kingdom it is more standardized and spread globally. This study confirms the effectiveness of both models in classifying slang words as well as identifying key trends in their use on social media. The contribution of this research is important for the study of digital linguistics because it expands the understanding of the dynamics of online slang use, and shows the great potential of NLP applications in linguistic analysis in the digital age. With the results obtained, this research can be a valuable guide for researchers and practitioners interested in understanding the evolution of language on social media, while providing a foundation for the development of more sophisticated and adaptive NLP technologies in handling language variations on digital platforms.]]>
