Half-life of Youtube News Videos: Diffusion Dynamics and Predictive Factors

TL;DR

This study analyzes the early diffusion patterns of YouTube news videos within the first 24 hours, quantifies their half-life, and explores predictive modeling using statistical and deep learning techniques.

Contribution

It introduces the first large-scale dataset and analysis of 24-hour diffusion half-life of YouTube news videos, along with predictive models and explainability insights.

Findings

Average 24-hour half-life is approximately 7 hours.

Significant variance in half-life across countries, from 2 to 15 hours.

Deep learning models outperform statistical models in predicting half-life.

Abstract

Consumption of YouTube news videos significantly shapes public opinion and political narratives. While prior works have studied the longitudinal dissemination dynamics of YouTube News videos across extended periods, limited attention has been paid to the short-term trends. In this paper, we investigate the early-stage diffusion patterns and dispersion rate of news videos on YouTube, focusing on the first 24 hours. To this end, we introduce and analyze a rich dataset of over 50,000 videos across 75 countries and six continents. We provide the first quantitative evaluation of the 24-hour half-life of YouTube news videos as well as identify their distinct diffusion patterns. According to the findings, the average 24-hour half-life is approximately 7 hours, with substantial variance both within and across countries, ranging from as short as 2 hours to as long as 15 hours. Additionally, we explore the problem of predicting the latency of news videos' 24-hour half-lives. Leveraging the presented datasets, we train and contrast the performance of 6 different models based on statistical as well as Deep Learning techniques. The difference in prediction results across the models is traced and analyzed. Lastly, we investigate the importance of video- and channel-related predictors through Explainable AI (XAI) techniques. The dataset, analysis codebase and the trained models are released at http://bit.ly/3ILvTLU to facilitate further research in this area.