USING VIDEO TITLES TO PREDICT YOUTUBE AUDIENCE BEHAVIOR BASED ON MACHINE LEARNING

As the leading video-sharing platform, YouTube has evolved into a new form of media, facilitating content dissemination by creators and fostering active audience participation. Enhanced viewer engagement offers creators increased influence and financial benefits due to heightened popularity. However, the vast quantity of videos available on YouTube means that only a select few manage to captivate significant audience attention. Within YouTube's interface, video titles play a crucial role in attracting users, making the exploration of the relationship between audience engagement and video titles an important research topic. This study aims to predict audience engagement by analyzing video titles using various machine learning methods. The study employs the Linguistic Inquiry and Word Count (LIWC) software for natural language processing, which calculates the frequency of word categories in the text, such as pronouns, emotional, and cognitive words, presenting these frequencies as relative percentages. Results indicate that different textual categories within video titles correlate significantly with audience liking and commenting behaviors. Among the evaluated machine learning techniques, Random Forest and K-Nearest Neighbors Regression models exhibit superior predictive performance. The findings provide insights into the intricate interplay between textual features of video titles and their impact on audience engagement. This research serves as a valuable reference for video creators, guiding their decision-making process concerning video titles to optimize audience interaction. Additionally, the study contributes to existing literature by elucidating the nuanced relationship between textual elements in video titles and audience responsiveness.