Emoji Reactions on Telegram: Unreliable Indicators of Emotional Resonance

TL;DR

This study investigates emoji reactions on Telegram, revealing they often do not reflect the emotional tone of messages and are influenced by social factors, challenging their use as straightforward sentiment indicators.

Contribution

The paper provides a detailed analysis of emoji reactions, showing their mismatch with message sentiment and highlighting social dynamics affecting emoji engagement.

Findings

Positive reactions dominate regardless of message sentiment

Emoji reactions do not reliably mirror emotional resonance

Features influencing emoji engagement are identified

Abstract

Emoji reactions are a frequently used feature of messaging platforms, yet their communicative role remains understudied. Prior work on emojis has focused predominantly on in-text usage, showing that emojis embedded in messages tend to amplify and mirror the author's affective tone. This evidence has often been extended to emoji reactions, treating them as indicators of emotional resonance or user sentiment. However, they may reflect broader social dynamics. Here, we investigate the communicative function of emoji reactions on Telegram. We analyze over 650k crypto-related messages that received at least one reaction, annotating each with sentiment, emotion, persuasion strategy, and speech act labels, and inferring the sentiment and emotion of emoji reactions using both lexicons and LLMs. We uncover a systematic mismatch between message and reaction sentiment, with positive reactions dominating even for neutral or negative content. This pattern persists across rhetorical strategies and emotional tones, indicating that emojis used as reactions do not reliably function as indicators of emotional mirroring or resonance of the content, in contrast to findings reported for in-text emojis. Finally, we identify the features that most predict emoji engagement. Overall, our findings caution against treating emoji reactions as sentiment labels, highlighting the need for more nuanced approaches in sentiment and engagement analysis.