Emotion entrainment accounts for the synchronous convergence of human emotions. Since the specific mechanisms of emotional entrainment are still unclear, the study examines the massive emotion entrainment patterns and uncovers the underlying dynamic mechanisms in the context of social media. We elaborate a pragmatic framework to characterize and quantify online entrainment phenomenon. We find that the emotions of online users entrain through social networks. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. By examining their communications, we further obtain that users often form their relations via dual entrainment, while maintaining it through single entrainment. These findings are predictive of emotional proximity within dyads and could be used to leverage emotion prediction.
The concept of emotional entrainment suggests that collective emotions, in particular, the feeling of affective attunement with others, can increase the identification with a social group, which social media helps facilitate.
The following articles are recommended for further reading.
Further reading Von Scheve, C., Beyer, M., Ismer, S., Kozlowska, M., & Morawetz, C. (2013). Emotional entrainment, national symbols, and identification: A naturalistic study around the men’s football World Cup. Current Sociology, 0011392113507463.  Lee C-C, Katsamanis A, Black MP, Baucom BR, Georgiou PG, Narayanan S (2011). An Analysis of PCA-Based Vocal Entrainment Measures in Married Couples’ Affective Spoken Interactions. Interspeech. pp. 3101–3104.  Varni G, Camurri A, Coletta P, Volpe G (2008). Emotional entrainment in music performance. Automatic Face & Gesture Recognition, 2008 FG’08 8th IEEE International Conference on: IEEE. pp. 1–5.
Link to the primary paper
He, S., Zheng, X., Zeng, D., Luo, C., & Zhang, Z. (2016). Exploring entrainment patterns of human emotion in social media. PLOS One, 11(3), e0150630. DOI: 10.1371/journal.pone.0150630
Xiaolong Zheng is an Associate Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences in Beijing. His research interests include complex networks, social computing, and data mining. Specifically, he is interested in social dynamics, social influence, spread dynamics, and opinion and behavior mining in social media.
The first author, Saike He is an Assistant Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences in Beijing. His research interests include sentiment analysis, behavior modeling, information diffusion, and synchronization in complex networks.