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.

Political campaigns increasingly rely on targeted strategies to influence voters on social media. Often, such campaigns have been studied by analysing coordinated behaviour to identify communities of users who exhibit similar patterns. While these analyses are typically conducted on static networks, recent extensions to temporal networks allow tracking users who change communities over time, opening new opportunities to quantitatively study influence in social networks. As a first step toward this goal, we analyse the messages users were exposed to during the UK 2019 election, comparing those received by users who shifted communities with others covering the same topics.Our findings reveal 54 statistically significant linguistic differences and show that a subset of persuasion techniques, including loaded language, exaggeration and minimization, doubt, and flag-waving, are particularly relevant to users’ shifts. This work underscores the importance of analysing coordination from a temporal and dynamic perspective to infer the drivers of users’ shifts in online debate.