Mia Huong Nguyen

SELECTED PUBLICATIONS

1. CHI

   🏆 VisceroHaptics: Investigating the Effects of Gut-based Audio-Haptic Feedback on Gastric Feelings and Gastric Interoceptive Behaviors

   Mia Huong Nguyen, Moritz Alexander Messerschmidt, Jochen Huber, and 1 more author

   In Proceedings of the CHI Conference on Human Factors in Computing Systems. 2026
2. CHI

   “My Tummy Has a Little Dragon”: From Everyday Experiences of Gut Sounds to Interoceptive Interaction Design

   Nandini Pasumarthy, Mia Huong Nguyen, Ashika Ramesh Krishnan, and 6 more authors

   In Proceedings of the CHI Conference on Human Factors in Computing Systems 2026
3. IJHCS

   Beyond Happy and Sad: Exploring Granular Affect Labelling to Enhance Emotion Regulation Ability

   Mia Huong Nguyen, Dixon Daniel Prem Rajendran, and Suranga Nanayakkara

   International Journal of Human-Computer Studies 2026
4. CHI

   GutIO: Toward Sensing and Inducing Gut Feelings with Abdominal Sounds

   Mia Huong Nguyen, Moritz Alexander Messerschmidt, Chitralekha Gupta, and 1 more author

   In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems 2025

   PDF
5. EMNLP

   EMO-KNOW: A Large Scale Dataset on Emotion-Cause

   Mia Huong Nguyen, Yasith Samaradivakara, Prasanth Sasikumar, and 2 more authors

   In Findings of the Association for Computational Linguistics: EMNLP Dec 2023

   Abs PDF Code

   Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. To bridge this gap, we introduce a large-scale dataset of emotion causes, derived from 9.8 million cleaned tweets over 15 years. We describe our curation process, which includes a comprehensive pipeline for data gathering, cleaning, labeling, and validation, ensuring the dataset’s reliability and richness. We extract emotion labels and provide abstractive summarization of the events causing emotions. The final dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. The novelty of our dataset stems from its broad spectrum of emotion classes and the abstractive emotion cause that facilitates the development of an emotion-cause knowledge graph for nuanced reasoning. Our dataset will enable the design of emotion-aware systems that account for the diverse emotional responses of different people for the same event.