The dynamics of emotions in online interaction.

R Soc Open Sci

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

Published: August 2016

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5108936PMC
http://dx.doi.org/10.1098/rsos.160059DOI Listing

Publication Analysis

Top Keywords

valence arousal
12
online
8
emotions online
8
emotional states
8
participating online
8
online discussions
8
model online
8
dynamics valence
8
emotional
5
arousal
5

Similar Publications

Contagious crying in infants has been considered an early marker of their sensitivity to others' emotions, a form of emotional contagion, and an early basis for empathy. However, it remains unclear whether infant distress in response to peer distress is due to the emotional content of crying or acoustically aversive properties of crying. Additionally, research remains severely biased towards samples from Europe and North America.

View Article and Find Full Text PDF

Predicting image memorability from evoked feelings.

Behav Res Methods

January 2025

Department of Psychology, Columbia University, New York, NY, USA.

While viewing a visual stimulus, we often cannot tell whether it is inherently memorable or forgettable. However, the memorability of a stimulus can be quantified and partially predicted by a collection of conceptual and perceptual factors. Higher-level properties that represent the "meaningfulness" of a visual stimulus to viewers best predict whether it will be remembered or forgotten across a population.

View Article and Find Full Text PDF

Colour is an integral part of natural and constructed environments. For many, it also has an aesthetic appeal, with some colours being more pleasant than others. Moreover, humans seem to systematically and reliably associate colours with emotions, such as yellow with joy, black with sadness, light colours with positive and dark colours with negative emotions.

View Article and Find Full Text PDF

Background: Recognition of emotion changes is of great significance to a person's physical and mental health. At present, EEG-based emotion recognition methods are mainly focused on time or frequency domains, but rarely on spatial information. Therefore, the goal of this study is to improve the performance of emotion recognition by integrating frequency and spatial domain information under multi-frequency bands.

View Article and Find Full Text PDF

Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.

Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!