Periodicity Detection of Emotional Communities in Microblogging

Social media allow users convey emotions, which are often related to real-world events, social relationships or personal experiences. Indeed, emotions can determine the propension of the users to socialize or attend events. Similarly, interactions with people can influence the personality and feelings of the individuals. Therefore, studying emotional content generated by the users can reveal information on the behavior of users or collectives of users. However, such an information is related only to a specific moment when the emotions are sporadic or episodic, therefore they could have little usefulness. On the contrary, it can have greater significance tracing emotions over time and understanding whether they may appear with regularity or whether they are associated to behaviors already observed in past and could recur. In this paper, we focus on the periodicity with which emotional words appear in the micro-blogs as indication of a collective emotional behavior expressed with regularity. We propose a computational solution that builds a cyberspace based on the emotional content produced by the users and determines communities of users who express with periodicity similar emotional behaviors. We show the viability of the method on the data of the social media platform Twitter and provide a quantitative evaluation and qualitative considerations.