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.
翻译:表情符号反应是即时通讯平台中频繁使用的功能,但其在沟通中的作用尚未得到充分研究。先前关于表情符号的研究主要集中于文本内使用,表明嵌入消息中的表情符号倾向于放大并反映作者的情感基调。这些证据常被推广至表情符号反应,将其视为情绪共鸣或用户情感态度的指标。然而,它们可能反映更广泛的社会动态。本文研究了Telegram平台上表情符号反应的沟通功能。我们分析了超过65万条获得至少一次反应的加密货币相关消息,为每条消息标注情感倾向、情绪类别、说服策略和言语行为标签,并同时使用词典和大型语言模型推断表情符号反应的情感倾向与情绪。我们发现消息情感与反应情感之间存在系统性错配:即使对于中性或负面内容,正面反应仍占主导地位。这种模式在不同修辞策略和情感基调中持续存在,表明作为反应使用的表情符号并不能可靠地反映对内容的情感镜像或共鸣,这与文本内表情符号的研究结论形成对比。最后,我们识别出最能预测表情符号参与度的特征。总体而言,我们的研究结果警示不应将表情符号反应简单视为情感标签,并强调在情感分析与参与度研究中需要采用更细致的方法。