# AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations

arXiv: https://arxiv.org/abs/2101.12284 · HTML: https://mlsanity.com/paper/2101.12284/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2021.

## Built on by (most cited first, 7 of 7)

- [Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?](https://mlsanity.com/paper/2303.07242/) (2023)
- [Metamorpheus: Interactive, Affective, and Creative Dream Narration Through Metaphorical Visual Storytelling](https://mlsanity.com/paper/2403.00632/) (2024)
- [MeetMap: Real-Time Collaborative Dialogue Mapping with LLMs in Online Meetings](https://mlsanity.com/paper/2502.01564/) (2025)
- [Not My Truce: Personality Differences in AI-Mediated Workplace Negotiation](https://mlsanity.com/paper/2604.00464/) (2026)
- [Documenting use cases in the affective computing domain using Unified Modeling Language](https://mlsanity.com/paper/2209.09666/) (2022)
- [Personal Productivity and Well-being -- Chapter 2 of the 2021 New Future of Work Report](https://mlsanity.com/paper/2103.02524/) (2021)
- [Designing AI-Supported Focus Groups: A Role x Modality Playbook](https://mlsanity.com/paper/2606.11835/) (2026)

## Abstract (arXiv)

The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N = 175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N = 117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.