Community Conversation
Community conversation analysis focuses on understanding the dynamics and nuances of human interaction within groups, aiming to extract meaningful information about social norms, emotions, and intentions. Current research employs various deep learning models, including transformer networks and graph neural networks, to analyze multimodal data (text, audio, video) and predict aspects like emotion, empathy, and the presence of manipulation or toxicity. This field is significant for advancing human-computer interaction, improving social robot design, and developing tools for community needs assessment and conflict resolution in diverse settings.
Papers
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
Ritam Dutt, Zhen Wu, Kelly Shi, Divyanshu Sheth, Prakhar Gupta, Carolyn Penstein Rose
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations
Yuntao Shou, Wei Ai, Jiayi Du, Tao Meng, Haiyan Liu, Nan Yin