This article provides a comprehensive investigation on the relations between
virality of news articles and the emotions they are found to evoke. Virality,
in our view, is a phenomenon with many facets, i.e. under this generic term
several different effects of persuasive communication are comprised. By
exploiting a high-coverage and bilingual corpus of documents containing metrics
of their spread on social networks as well as a massive affective annotation
provided by readers, we present a thorough analysis of the interplay between
evoked emotions and viral facets. We highlight and discuss our findings in
light of a cross-lingual approach: while we discover differences in evoked
emotions and corresponding viral effects, we provide preliminary evidence of a
generalized explanatory model rooted in the deep structure of emotions: the
Valence-Arousal-Dominance (VAD) circumplex. We find that viral facets appear to
be consistently affected by particular VAD configurations, and these
configurations indicate a clear connection with distinct phenomena underlying
persuasive communication.

通过分析社交媒体上遭受的情绪和传播度，本文探讨了新闻文章的传播和情感之间的关系，并发现情感是影响新闻传播和传染力的根本。

情感主题：情感与病毒传播之间关系的大规模跨语言研究

Deep Feelings: A Massive Cross-Lingual Study on the Relation between  Emotions and Virality

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.

本文旨在阐明病毒传播的概念，结合社交网络中内容的传播属性，通过机器学习方法初步分析预测病毒传播的影响因素。