# PAGAN: Video Affect Annotation Made Easy

arXiv: https://arxiv.org/abs/1907.01008 · HTML: https://mlsanity.com/paper/1907.01008/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
- [Generative Personas That Behave and Experience Like Humans](https://mlsanity.com/paper/2209.00459/) (2022)
- [Play with Emotion: Affect-Driven Reinforcement Learning](https://mlsanity.com/paper/2208.12622/) (2022)
- [The Invariant Ground Truth of Affect](https://mlsanity.com/paper/2210.07630/) (2022)
- [GameVibe: A Multimodal Affective Game Corpus](https://mlsanity.com/paper/2407.12787/) (2024)
- [Can Large Language Models Capture Video Game Engagement?](https://mlsanity.com/paper/2502.04379/) (2025)
- [Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers](https://mlsanity.com/paper/2408.06346/) (2024)
- [Emotions as Ambiguity-aware Ordinal Representations](https://mlsanity.com/paper/2508.19193/) (2025)
- [Do Vision Language Models Understand Human Engagement in Games?](https://mlsanity.com/paper/2603.18480/) (2026)
- [E3mo-Bench: A Scalable Benchmark for Multimodal Evoked and Expressed Emotion Understanding via Bayesian Pairwise Alignment](https://mlsanity.com/paper/2608.10796/) (2026)
- [PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation](https://mlsanity.com/paper/2601.13904/) (2026)
- [Resource-Constrained Affect Modelling via Variance Regularisation Pruning](https://mlsanity.com/paper/2605.27479/) (2026)

## Abstract (arXiv)

How could we gather affect annotations in a rapid, unobtrusive, and accessible fashion? How could we still make sure that these annotations are reliable enough for data-hungry affect modelling methods? This paper addresses these questions by introducing PAGAN, an accessible, general-purpose, online platform for crowdsourcing affect labels in videos. The design of PAGAN overcomes the accessibility limitations of existing annotation tools, which often require advanced technical skills or even the on-site involvement of the researcher. Such limitations often yield affective corpora that are restricted in size, scope and use, as the applicability of modern data-demanding machine learning methods is rather limited. The description of PAGAN is accompanied by an exploratory study which compares the reliability of three continuous annotation tools currently supported by the platform. Our key results reveal higher inter-rater agreement when annotation traces are processed in a relative manner and collected via unbounded labelling.