Exploring Text Virality in Social Networks

TL;DR

This paper investigates the nature of virality in social networks, emphasizing content features over influencers and revealing its multifaceted nature through initial machine learning experiments.

Contribution

It introduces a new perspective on virality, highlighting its dependence on content rather than influencers and its multiple facets, supported by experimental evidence.

Findings

Virality is more related to content than influencers.

Virality comprises multiple, partially overlapping effects.

Content features can independently predict aspects of virality.

Abstract

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.