Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality
This research addresses the problem of understanding viral content for social media analysts and communicators, but it is incremental as it builds on existing emotion models.
The study investigated how emotions in news articles relate to their virality on social networks, using a bilingual corpus with affective annotations, and found that specific configurations of the Valence-Arousal-Dominance model consistently affect viral facets.
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.