Exploring Entrainment Patterns of Human Emotion in Social Media

Abstract

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.

Trace of pace, place, and space in personal relationships: The chronogeometrics of studying relationships at scale

“…As social networks expand in size, a large number of human producers and consumers will become less reciprocal due to time displacement. In a study of over 1 million users and 18 million English blog posts on http://livejournal.com and 1.7 million users and 3.5 million messages on China's Sina Weibo, He, Zheng, Zeng, Luo, and Zhang () examined emotion entrainment, or the evolving synchronous convergence of expressed emotion in those messages. They found that, despite different patterns and lifespans between the two platforms, entrainment followed a consistent three‐stage process in which, initially, one person proactively entrained toward the other, at which point the individuals mutually entrained toward each other, and once the relationship was established, entrainment became more asymmetric again.…”

Sensorial discourse and corpus in the digital humanities era: The example of the wine language

“…An important part of the selling process for wine is based on storytelling (Lamarre, 2018) about the wine-making process and the winemaker's history (Bach, 2017a), playing with emotive responsiveness of the clients; if the seller cannot activate this part or if clients break the emotional bond between them and the seller, the sale is more likely to be unsuccessful. At the same time, recent research has shown that analogical emotions and emotion contagion can be transferred into the virtual world (He et al, 2016).…”

A Novel Neuro-Fuzzy Model to Detect Human Emotions Using Different Set of Vital Factors with Performance Index Measure

“…The optimal set of factors can be extended to a number of emotions as well [2]. For example, the factors 1, 2, 3, and 4 are dominant for emotions 12,14,18,20,21,22 for K=7, indicating that 6 emotions (Affection, Contentment, Antic Pleasure Imagery, Relief, Surprise, and Suspense) can be detected with 7 factors, of which 1, 2, 3, and 4 (Electroencephalography, Heart rate, Heart rate variability, Pre-ejection period) are common for all [2].…”

“…The detection of such emotional responses relies mainly on several vital factors such as Heart [1-3, 9, 10]. Human emotion detection models can be readily used in conjunction with social networks such as Facebook, Twitter, Myspace, LinkedIn, Instagram, and messenger applications, amongst others, by annunciating the emotional statuses between communicating parties [1,20]. Other suitable arenas would include health care applications readily disseminating vital health signs between patients and their care providers [1].…”