Abstract
This study examines how reaction emojis evolve beyond their traditional emotional associations within a large Russian-language Telegram community. Using lexicon-based sentiment scoring, temporal frequency charts, and cluster analysis, we analyzed 220,972 reacted comments from August 2021 to April 2025, focusing on four high-frequency reactions: "thumbs-up", "thumbs-down", "red heart", and "clown face". Our findings revealed that emoji meaning is not constant and can change over time. Spikes in the use of certain emoji reactions coincide with periods of social turbulence, indicating them in real time and potentially enabling prediction. These findings expose community-driven semantic shift and demonstrate that reaction patterns provide weak supervision cues for identifying sentiment-context mismatches, which aids in moderation and crisis detection. The results are also important for training neural network models using community-annotated messages.
Paper Type
Poster
DOI
10.62036/ISD.2025.93
Context aware evolution of emoji sentiment reactions in a large Telegram community
This study examines how reaction emojis evolve beyond their traditional emotional associations within a large Russian-language Telegram community. Using lexicon-based sentiment scoring, temporal frequency charts, and cluster analysis, we analyzed 220,972 reacted comments from August 2021 to April 2025, focusing on four high-frequency reactions: "thumbs-up", "thumbs-down", "red heart", and "clown face". Our findings revealed that emoji meaning is not constant and can change over time. Spikes in the use of certain emoji reactions coincide with periods of social turbulence, indicating them in real time and potentially enabling prediction. These findings expose community-driven semantic shift and demonstrate that reaction patterns provide weak supervision cues for identifying sentiment-context mismatches, which aids in moderation and crisis detection. The results are also important for training neural network models using community-annotated messages.
Recommended Citation
Mints, O. (2025). Context aware evolution of emoji sentiment reactions in a large Telegram communityIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.93