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Title: ThingMoji : user-captured cut-outs for in-stream visual communication
Authors: Hu, E
Wan, Q
Zhou, C
Azim, AR
Wang, P
Hu, X 
Zeng, Y
Lu, Z
Heo, S
Issue Date: Nov-2025
Source: ACM proceedings on human-computer interaction, Nov. 2025, v. 9, no. 7, CSCW495
Abstract: Live streaming has become increasingly popular, driven by the desire for direct and real-time interactions between streamers and viewers. However, current text-based interactions and pre-defined emojis limit expressiveness, especially when referring to specific stream moments. We propose ThingMoji, a type of user-captured cut-outs to enhance user expression and foster more effective communication between streamers and their audience in the comment section. ThingMojis are unique digital icons created by users by capturing snapshots and annotating specific areas at any point during the stream. We developed StreamThing, a live-streaming platform integrated with ThingMojis, to explore their use during object-focused live streaming contexts. In a user study with three in-the-wild deployments reveals the expressive use of ThingMojis in diverse live-streaming scenarios with rich visual contents. Our findings show that ThingMojis enable viewers to reference specific objects, express emotions, and create shared visual narratives. Streamers found ThingMojis valuable for facilitating on-the-fly communication around visual content and fostering playful interactions. The study also uncovered challenges in ThingMoji comprehension, issues for long-term uses of ThingMojis, and potential concerns regarding misuse. Based on these insights, we discussed new opportunities for supporting object-focused communication during live streaming environments.
Keywords: Human-AI
Live-streaming
One-to- many communication
Shared narrative
Video-mediated communication
Publisher: Association for Computing Machinery
Journal: ACM proceedings on human-computer interaction 
EISSN: 2573-0142
DOI: 10.1145/3757676
Rights: This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0).
© 2025 Copyright held by the owner/author(s).
The following publication Hu, E., Wan, Q., Zhou, C., Azim, M. A. R., Wang, P., Hu, X., Zeng, Y., Lu, Z., & Heo, S. (2025). ThingMoji: User-Captured Cut-Outs For In-Stream Visual Communication. Proc. ACM Hum.-Comput. Interact., 9(7), Article CSCW495 is available at https://doi.org/10.1145/3757676.
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