In the mood: the dynamics of collective sentiments on Twitter

TL;DR

This study analyzes how Twitter users' sentiments relate to evolving network structures, revealing that influential users exhibit distinct sentiment patterns, community sentiments are stable over time unless affected by external events, and a model can replicate observed emotive dynamics.

Contribution

It introduces a novel analysis of sentiment dynamics in Twitter networks, linking user influence, community stability, and external events, and develops a calibrated agent-based model.

Findings

Influential users use more positive sentiment and less negative sentiment.

Community sentiment remains stable over months unless external events cause changes.

A simple agent-based model can reproduce observed emotive response patterns.

Abstract

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.