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在线互动中的情绪动态。

The dynamics of emotions in online interaction.

作者信息

Garcia David, Kappas Arvid, Küster Dennis, Schweitzer Frank

机构信息

Chair of Systems Design, ETH Zurich , Weinbergstrasse 56/58, 8092 Zurich, Switzerland.

Jacobs University Bremen , Campus Ring 1, 28759 Bremen, Germany.

出版信息

R Soc Open Sci. 2016 Aug 10;3(8):160059. doi: 10.1098/rsos.160059. eCollection 2016 Aug.

Abstract

We study the changes in emotional states induced by reading and participating in online discussions, empirically testing a computational model of online emotional interaction. Using principles of dynamical systems, we quantify changes in valence and arousal through subjective reports, as recorded in three independent studies including 207 participants (110 female). In the context of online discussions, the dynamics of valence and arousal is composed of two forces: an internal relaxation towards baseline values independent of the emotional charge of the discussion and a driving force of emotional states that depends on the content of the discussion. The dynamics of valence show the existence of positive and negative tendencies, while arousal increases when reading emotional content regardless of its polarity. The tendency of participants to take part in the discussion increases with positive arousal. When participating in an online discussion, the content of participants' expression depends on their valence, and their arousal significantly decreases afterwards as a regulation mechanism. We illustrate how these results allow the design of agent-based models to reproduce and analyse emotions in online communities. Our work empirically validates the microdynamics of a model of online collective emotions, bridging online data analysis with research in the laboratory.

摘要

我们研究了阅读和参与在线讨论所引发的情绪状态变化,通过实证检验了一个在线情绪互动的计算模型。利用动力系统原理,我们通过主观报告量化效价和唤醒度的变化,这些报告记录在三项独立研究中,共有207名参与者(110名女性)。在在线讨论的背景下,效价和唤醒度的动态由两种力量组成:一种是向与讨论的情感负荷无关的基线值的内部松弛,另一种是取决于讨论内容的情绪状态驱动力。效价的动态显示出正负倾向的存在,而无论情绪内容的极性如何,阅读时唤醒度都会增加。参与者参与讨论的倾向随着积极的唤醒度而增加。当参与在线讨论时,参与者表达的内容取决于他们的效价,并且之后他们的唤醒度会显著降低,作为一种调节机制。我们说明了这些结果如何允许设计基于智能体的模型来再现和分析在线社区中的情绪。我们的工作通过实证验证了在线集体情绪模型的微观动态,将在线数据分析与实验室研究联系起来。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52f6/5108936/139c133a000b/rsos160059-g4.jpg

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