Quantifying the Effect of Sentiment on Information Diffusion in Social Media

TL;DR

This study investigates how sentiment influences information spread on social media, revealing that negative messages spread faster while positive messages reach larger audiences, with different emotions associated with event types.

Contribution

It provides a rigorous analysis of sentiment's role in social media diffusion, highlighting distinct patterns for positive and negative content and their relation to event anticipation.

Findings

Negative messages spread faster than positive ones.

Positive messages reach larger audiences.

Different sentiment patterns are associated with event types.

Abstract

Social media have become the main vehicle of information production and consumption online. Millions of users every day log on their Facebook or Twitter accounts to get updates and news, read about their topics of interest, and become exposed to new opportunities and interactions. Although recent studies suggest that the contents users produce will affect the emotions of their readers, we still lack a rigorous understanding of the role and effects of contents sentiment on the dynamics of information diffusion. This work aims at quantifying the effect of sentiment on information diffusion, to understand: (i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); (ii) what kind of emotions are more typical of popular conversations on social media; and, (iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics. Our findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-called positive bias. As for the entire conversations, we highlight how different temporal dynamics exhibit different sentiment patterns: for example, positive sentiment builds up for highly-anticipated events, while unexpected events are mainly characterized by negative sentiment. Our contribution is a milestone to understand how the emotions expressed in short texts affect their spreading in online social ecosystems, and may help to craft effective policies and strategies for content generation and diffusion.