Toward Real-Time Cohesion Estimation in Hybrid Groups: A Multimodal Social Signal Processing Approach
Year: 2025
Type: conference-paper
Abstract: Cohesion is a multidimensional construct reflecting a shared commitment to group task(s) and strong interpersonal bonds.It plays a crucial role in collective success and emotional well-being.In human-computer interactions, embodied conversational agents can help foster group cohesion and prevent negative group dynamics by detecting and predicting low cohesion in real-time.This study introduces a novel social signal processing framework for real-time cohesion estimation in hybrid group interactions and develops computational models based on it.Using a multiparty interaction corpus, we extract nonverbal social signals available in real-time and focus on estimating overall cohesion and its dimensions (social, task).We further examine the contribution of social signals to the accuracy of low cohesion detection in collaborative interactions.In our experimental setting, overall low cohesion is more effectively detected than its social or task dimensions.The ablation study suggests that specific social signals reflecting interindividual interaction patterns substantially contribute to low cohesion detection performance.This work demonstrates the potential of data-driven methods for real-time cohesion estimation in multiparty interactions, highlighting promising opportunities for ECAs to restore and maintain cohesion in hybrid group settings.
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4414959751