  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.
