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 S enti S trength 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 with those obtained from our empirical dataset.
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机译：我们研究了Twitter用户的情感水平与用户通过@提及彼此创建的不断发展的网络结构之间的关系。我们使用大量的推文数据集，在其中应用了三种情感评分算法，其中包括开源S enti S trength程序。具体来说，我们做出了三点贡献。首先，我们发现具有最大传播潜力的人（根据动态中心度度量）使用的情感与普通用户的使用不同：例如，他们使用正面情感的频率更高，而使用负面情感的频率更低。其次，我们发现，当我们在几个月内关注结构稳定的Twitter社区时，其情绪水平也是稳定的，并且在大多数情况下，一天到一天之间社区情绪的突然变化可以追溯到影响社区的外部事件。第三，基于我们的发现，我们创建并校准了一个简单的基于主体的模型，该模型能够重现与从经验数据集获得的情绪反应相当的情绪反应。
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