Sociolinguistic Analysis of Gen Alpha’s Slang in YouTube Shorts Comments of Meme Content Creators

This study examines the use of Gen Alpha slang in YouTube short videos focusing on the importance of linguistic and sociolinguistic patterns. The purpose of this study is to identify slang used by Gen Alpha, analyze the types of slang based on Robert L. Chapman's classification. This study uses a qualitative method with a case study design that analyzes the comments column of content from five meme creators on YouTube Shorts. The choice of YouTube shorts as a data source is based on the popularity of this platform among Gen Alpha and this platform that encourages fast and dynamic communication. Data collection was carried out in Miles and Huberman's theory, data reduction was applied to filter relevant comments, data display was used to classify slang terms based on categories proposed by Robert L. Chapman, and verification was carried out through triangulation with other sociolinguistic references. The 121 comments containing slang, 75 were categorized as primary slang and 46 as secondary slang. This analysis is based on Janet Holmes’ sociolinguistic theory of language change and age grading, and Chapman’s slang word classification, which divides slang into primary and secondary categories. The results showed that Gen Alpha frequently used slang terms such as “Sigma”, “Rizz”, and “Mewing.” The primary slang words such as “Sigma” and “Rizz” count for 35% of the total data collected. Secondary slang, more accurate to online subcultures, includes 25% words such as “Ohio” and “Fanumtax.” The study concluded that YouTube Shorts act as a digital space where Gen Alpha innovate in language and enhance their group identity and social interactions. This study contributes to the field of sociolinguistics by providing insight into the effects of digital media on the development of slang and how language works to shape social dynamics among the younger generation. The results of this study suggest that digital slang has a significant impact on language development by accelerating the spread of new terms and influencing the types of communication on online platforms.