Author(s)

YaWei He^1*, ZiYi Guan¹, Wen Liu^2,3

Affiliation(s)

¹School of Media and Communication, Shanghai Jiao Tong University, Shanghai 200240, China.

²AI Technology Innovation Group, School of Economics and Management, Communication University of China, Beijing 100024, China.

³Department of Engineering, University of Cambridge, Cambridge CB3 0FS, United Kingdom.

Corresponding Author

YaWei He

ABSTRACT

In the highly competitive ecosystem of online video platforms, metadata elements such as titles are critical determinants of user engagement. This study uses computational communication methods to explore the interactive effects of title sentiment intensity and title length on video view counts for automotive videos on the Bilibili platform. Through hierarchical regression analysis on a large-scale dataset of 984 videos, and supported by non-parametric Generalized Additive Models (GAM) and bootstrap analysis for robustness, this research reveals two key findings: First, there is a significant inverted U-shaped relationship between title length and video views, meaning views increase with length up to a peak at approximately 26-30 characters, and then decline. Second, title length plays a negative moderating role in the relationship between sentiment intensity and views. This finding suggests that emotional language is more effective in attracting user attention in short titles, but its appeal diminishes in longer, more information-dense titles, and may even have a negative impact if perceived as "clickbait." These findings  offer  nuanced,  data–driven  strategies  for  content  creators  and  digital  marketers  for optimizing video metadata to maximize audience engagement on competitive digital platforms.

KEYWORDS

User engagement; Sentiment analysis; Video analytics; Title optimization; Moderation analysis; Computational communication

CITE THIS PAPER

YaWei He, ZiYi Guan, Wen Liu. The interactive effects of title sentiment and length on video dissemination: optimal length and the diminishing returns of emotion. Journal of Computer Science and Electrical Engineering. 2025, 7(6): 30-40. DOI: https://doi.org/10.61784/jcsee3088.

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