Exploring Text Virality in Social Networks
This work addresses the challenge of predicting and analyzing virality for social media platforms and researchers, but it appears incremental as it builds on existing concepts with preliminary experiments.
The paper tackles the problem of understanding virality in social networks by arguing that it is primarily driven by content nature rather than influencers, and that it comprises multiple distinct effects. They provide initial experiments using machine learning to predict different aspects of virality based on content features.
This paper aims to shed some light on the concept of virality - especially in social networks - and to provide new insights on its structure. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread, rather than to the influencers who spread it, (b) virality is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised and they only partially overlap. To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be independently predicted according to content features.