# MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions

arXiv: https://arxiv.org/abs/2209.09578 · HTML: https://mlsanity.com/paper/2209.09578/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2022.

## Built on by (most cited first, 10 of 10)

- [Data Augmentation for Human Behavior Analysis in Multi-Person Conversations](https://mlsanity.com/paper/2308.01526/) (2023)
- [Backchannel Detection and Agreement Estimation from Video with Transformer Networks](https://mlsanity.com/paper/2306.01656/) (2023)
- [Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement](https://mlsanity.com/paper/2409.13726/) (2024)
- [Beyond Words: Multimodal LLM Knows When to Speak](https://mlsanity.com/paper/2505.14654/) (2025)
- [MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions](https://mlsanity.com/paper/2308.08256/) (2023)
- [EgoSocial: Benchmarking Proactive Intervention Ability of Omnimodal LLMs via Egocentric Social Interaction Perception](https://mlsanity.com/paper/2510.13105/) (2025)
- [Generalizable Engagement Estimation in Conversation via Domain Prompting and Parallel Attention](https://mlsanity.com/paper/2508.14448/) (2025)
- [Aligning Backchannel and Dialogue Context Representations via Contrastive LLM Fine-Tuning](https://mlsanity.com/paper/2604.16622/) (2026)
- [CMIS-Net: A Cascaded Multi-Scale Individual Standardization Network for Backchannel Agreement Estimation](https://mlsanity.com/paper/2510.17855/) (2025)
- [A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing](https://mlsanity.com/paper/2606.05330/) (2026)

## Abstract (arXiv)

Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroupInteraction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.