A Predictive Model for Online Content Virality: The Case of University of the Philippines Open University's Massive Open Online Courses Calendar

Abstract

In today's interconnected digital landscape, social media platforms drive the dissemination of online content. Cutting through this digital clutter to achieve visibility and resonance has become essential for effective communication on these platforms. A widely recognized but little-understood phenomenon in social media is "going viral," characterized by rapid and extensive dissemination across social circles. Understanding the factors that initiate viral spread typically occurs after the content has gained traction. However, there has yet to be a consensus on a universal model for predicting virality.

The study focused on identifying the key drivers of virality, understanding the interactions among these drivers, and developing a predictive model to anticipate content-sharing behavior. A quantitative methodology was employed, including a cross-sectional survey of 380 respondents who registered for UPOU MOOCs during a user surge from January 19 to March 2, 2023. Data collection was conducted through an online survey, and the analysis involved descriptive statistics, correlation analyses, and binary logistic regression to predict sharing behavior.

The study developed a predictive model that provides a comprehensive framework for understanding the dynamics of online content sharing. It highlights the interplay of external, intrapersonal, and interpersonal factors in driving the sharing of online content. The model emphasizes the significance of both online and offline sharing behaviors, demonstrating the lasting impact of word-of-mouth. This sharing behavior creates a social sharing infinity loop, where content perceived as relevant or useful continues to be disseminated, further enhancing its virality.

In conclusion, the study offers valuable insights into the complex dynamics of content virality, emphasizing the importance of understanding the various factors that influence sharing behavior. These insights can help optimize the reach and impact of online content.

Keywords: Virality; Predictive Model for Virality; Massive Open Online Courses; MOOCs; Enrollment Surge; Social Media; Facebook