Optimizing YouTube Video Discoverability Through Trend Analysis and Hashtag Generation
Chamodhya Manawathilake, Gamage Upeksha Ganegoda · 2024
YouTube has established itself as one of the most influential platforms in the digital media landscape, offering content creators an unparalleled opportunity to reach and engage with vast audiences. As the volume of content on YouTube continues to grow, creators are increasingly seeking strategies to improve the discoverability of their videos within this expansive ecosystem. Among the many factors that contribute to a video's visibility, hashtags have emerged as particularly powerful. Acting as navigational markers, hashtags link videos to relevant topics and communities, thereby increasing their chances of being discovered by target audiences. However, selecting and deploying effective hashtags is a complex process that requires careful alignment of trending themes, keywords, and the video's content. This research delves into the critical role that hashtags play in boosting video discoverability on YouTube, by analyzing YouTube trends through K-Means clustering and generating hashtags through the BERT language model. The effectiveness of the model is evaluated by analyzing engagement metrics such as view count, like count, and comment count, which are essential indicators of a video's reach and audience interaction.