This paper aims to provide new insights on the concept of virality and on its structure - especially in social networks. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread (b) virality is a phenomenon with many affective responses, i.e. under this generic term several different effects of persuasive communication are comprised. To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be predicted according to content features. We further provide a class-based psycholinguistic analysis of the features salient for virality components.
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