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\"Providing an in-depth discussion of emoji use in a global context, this volume presents the use of emoji as a hugely important facet of computer-mediated communication, leading author Jieun Kiaer to coin the term 'emoji speak'. Exploring why and how emojis are born, and the different ways in which people use them, this book highlights the diversity of emoji speak. Presenting the results of empirical investigations with participants of British, Belgian, Chinese, French, Japanese, Jordanian, Korean, Singaporean, and Spanish backgrounds, it raises important questions around the complexity of emoji use. Though emojis have become ubiquitous, their interpretation can be more challenging. What is humorous in one region, for example, might be considered inappropriate or insulting in another. Whilst emoji use can speed up our communication, we might also question whether they convey our emotions sufficiently. Moreover, far from belonging to the youth, people of all ages now use emoji speak, prompting Kiaer to consider the future of our communication in an increasingly digital world.\" -- Provided by publisher.

When we meet new people, we instinctively form a first impression of their characteristics, which can significantly impact interpersonal dynamics. The present study extends previous research examining face-to-face interactions by exploring impression formation in technology-mediated communication, where nonverbal cues are less abundant. Specifically, we examined the extent to which emotional interpretation of text messages influences attributions of dominance and warmth. Participants (N = 399) were shown text messages that conveyed a positive, negative, or neutral emotional signal. Some messages were accompanied by an emoji (happy, angry, or neutral), and others did not contain an emoji. Participants rated their perception of the message sender's emotional state, as well as how warm or dominant they thought the sender was. Results suggest that emotional cues, even in the absence of face-to-face visual or auditory feedback, played a role in impression formation. These findings shed light on the mechanisms of social cognition in technology-mediated communication and suggest strategies for achieving interpersonal goals in environments where traditional nonverbal cues are limited. Lorsque nous rencontrons de nouvelles personnes, nous nous faisons instinctivement une première impression de leurs caractéristiques, ce qui peut avoir un impact significatif sur la dynamique interpersonnelle. La présente étude s'inscrit dans le prolongement de recherches antérieures portant sur les interactions en face à face en explorant la formation d'une impression dans le cadre d'une communication médiatisée par la technologie, où les indices non verbaux sont moins abondants. Plus précisément, nous avons examiné la mesure dans laquelle l'interprétation émotionnelle des messages textes influence les attributions de dominance et de chaleur. Les participants (N = 399) ont reçu des messages textes qui transmettaient un signal émotionnel positif, négatif ou neutre. Certains messages étaient accompagnés d'un émoji (heureux, en colère ou neutre), tandis que d'autres ne contenaient pas d'émoji. Les participants ont évalué leur perception de l'état émotionnel de l'expéditeur du message, ainsi que le degré de chaleur ou de dominance qu'ils lui attribuaient. Les résultats suggèrent que les indices émotionnels, même en l'absence d'informations visuelles ou auditives, jouent un rôle dans la formation des impressions. Ces résultats mettent en lumière les mécanismes de la cognition sociale dans la communication assistée par la technologie et suggèrent des stratégies pour atteindre des objectifs interpersonnels dans des environnements où les indices non verbaux traditionnels sont limités. Public Significance Statement We examined how first impressions are formed in technology-mediated interactions such as texting, where traditional nonverbal cues are absent. We found that emojis-which can be used to convey emotions in place of traditional facial expressions-significantly influenced people's perceptions of how dominant and warm a sender was. These findings highlight how digital communication strategies can impact interpersonal judgements and offer insights into navigating social interactions effectively in online environments.

Sarcasm detection has gained significance in sentiment analysis, especially when social media is rife with cyberbullying and trolling. Emojis have garnered researchers’ interest as they are polysemic. But they are usually employed in combination with other modalities in sarcasm detection. This work proposes an emoji-focused approach to study their role in sarcasm detection. This structured approach studies how emojis, sentiment of text and emojis, most frequent emoji in text, and its position helps with sarcasm classification task. Experiments include various machine learning classifiers and BERT fine-tuned for emojis to reveal the decisive role of emojis in sarcasm detection, even in the absence of any other modality. Novel sarcasm-aware GloVe-based emoji embeddings are presented that outperform other available emoji embeddings to achieve highest F1, MCC, and RoC_AuC scores on two unseen datasets. Emoji embeddings, BERT fine-tuned with emojis, and emoji-focused models presented in this work can be used by researchers as baseline for sarcasm classification when situational or conversational context and other modalities like visuals, audio etc. are absent. This emoji-focused approach can be useful in identifying bullying or hateful content on public platforms and even on private chat-based platforms where users may more frequently imply sarcasm under the guise of emojis.