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Emoji speak : communication and behaviours on social media
\"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.
Book
Textual paralanguage and its implications for marketing communications
Both face-to-face communication and communication in online environments convey information beyond the actual verbal message. In a traditional face-to-face conversation, paralanguage, or the ancillary meaning- and emotion-laden aspects of speech that are not actual verbal prose, gives contextual information that allows interactors to more appropriately understand the message being conveyed. In this paper, we conceptualize textual paralanguage (TPL), which we define as written manifestations of nonverbal audible, tactile, and visual elements that supplement or replace written language and that can be expressed through words, symbols, images, punctuation, demarcations, or any combination of these elements. We develop a typology of textual paralanguage using data from Twitter, Facebook, and Instagram. We present a conceptual framework of antecedents and consequences of brands' use of textual paralanguage. Implications for theory and practice are discussed.
Journal Article
Let’s face it: When and how facial emojis increase the persuasiveness of electronic word of mouth
Facial emojis have increasingly permeated electronic word of mouth (eWOM), but the persuasive consequences of this phenomenon remain unclear. Drawing on emotions as social information (EASI) theory, this research reveals that facial emojis influence persuasion (e.g., product choice) by affecting emotional arousal and perceived ambiguity. While the effect through emotional arousal is generally positive, the effect through ambiguity depends on the emojis' function in eWOM: facial emojis that replace a verbal expression increase ambiguity and therefore reduce persuasion, whereas those that reiterate a verbal expression decrease ambiguity and therefore enhance persuasion. Both the emotional-arousal and ambiguity pathways determine the net persuasive effect. This research also explores two situations (high verbal context richness and eWOM from strong ties) where replacing facial emojis can increase persuasion. Finally, the authors show that facial emojis' persuasive power is generalizable to online brand communications, influencing key management outcomes such as click-through rates for digital ads.
Journal Article
Effective Automated Transformer Model based Sarcasm Detection Using Multilingual Data
Sarcasm detection is crucial for social media users to understand more about the underlying facts. However, determining the sarcasm only from text is not appropriate for recently updated social networks. It can be overcome by analyzing both the emoji and text data. Therefore, bilingual data in Hindi and English with emojis are offered as input to the proposed model. Traditionally, different transformer models were developed for efficient sarcasm detection, but such models haven’t reached a satisfactory position in the performance enhancement chart. Therefore, in this proposed model, the attention based transformer model is developed, which shows effective performance in analyzing both the emoji and text data. Using raw data in the transformer model will reduce the accuracy rate, therefore, to overcome such an issue, the pre-processing steps like stop word removal, case folding, filtering, lemmatization, stemming, and tokenization are initially performed over the input data. After pre-processing, the Average based Term Frequency-Inverse Document Frequency (ATF-IDF) approach is used to extract the textual features. The Gated Temporal Bidirectional Convolution Network (GT-BiCNet) is used to create the text model. The emoji-to-vector model (E-VM) is used to construct the Emoji model and express the features as vectors. The produced models obtained TexMoJ features concatenated using a deep feature fusion method. The resultant vectors are used to classify the feature vectors using the deep learning model Attention LSTM based on Amended Bidirectional Encoder Representation from Transformers (ALABerT). The network model’s losses are reduced by using the Enhanced Pelican Optimization Algorithm (EpoA). The softmax layer efficiently separates the data into sarcasm and non-sarcasm. The proposed method is compared to many current methodologies regarding various performances. The English Twitter dataset has attained 99.1% accuracy, 99.2% precision, 99.1% recall, 99.1% F-measure, an execution time of 56.66 s, and an average threshold of 12364.365 s. The accuracy, recall, precision, and F-measure of the Hindi Twitter dataset are 98.1%, 98.41%, 98.2%, and 69.6%, respectively.
Journal Article
The Conservatism of Emoji: Work, Affect, and Communication
This piece examines emoji as conduits for affective labor in the social networks of informational capitalism. Emoji, ubiquitous digital images that can appear in text messages, emails, and social media chat platforms, are rich in social, cultural, and economic significance. This article examines emoji as historical, social, and cultural objects, and as examples of skeuomorphism and of technical standardization. Now superseded as explicitly monetized objects by other graphics designed for affective interactions, emoji nonetheless represent emotional data of enormous interest to businesses in the digital economy, and continue to act symbolically as signifiers of affective meaning. We argue that emoji characters both embody and represent the tension between affect as human potential, and as a productive force that capital continually seeks to harness through the management of everyday biopolitics. Emoji are instances of a contest between the creative power of affective labor and its limits within a digital realm in the thrall of market logic.
Journal Article
Worth a thousand interpersonal words: Emoji as affective signals for relationship-oriented digital communication
Computer-mediated communication (CMC) is pervasive in our lives, influencing social interaction including human courtship. To connect with potential partners via CMC, modern relationship-seekers must master faster and shorter methods of communicating self-disclosure and affect. Although CMC can lack crucial sensory information in this context, emojis may provide useful aid. Across two studies, we assessed attitudes toward and frequency of emoji use, and whether signaling affect via emoji use relates to more romantic and sexual opportunities. Our findings suggest that emoji use with potential partners is associated with maintaining connection beyond the first date, and more romantic and sexual interactions over the previous year. This research provides evidence that emojis convey important affective information to potential partners, and are potentially associated with more successful intimate connection. Implications for multiple theoretical models and methodologies are discussed.
Journal Article
Emoji as gesture in digital communication: Emoji improve comprehension of indirect speech
As reliance on digital communication grows, so does the importance of communicating effectively with text. Yet when communicating with text, benefits from other channels, such as hand gesture, are diminished. Hand gestures support comprehension and disambiguate characteristics of the spoken message by providing information in a visual channel supporting speech. Can emoji (pictures used to supplement text communication) perform similar functions? Here, we ask whether emoji improve comprehension of indirect speech. Indirect speech is ambiguous, and appropriate comprehension depends on the receiver decoding context cues, such as hand gesture. We adapted gesture conditions from prior research (Kelly et al.,
1999
, Experiment 2) to a digital, text-based format, using emoji rather than gestures. Participants interpreted 12 hypothetical text-message exchanges that ended with indirect speech, communicated via text only, text+emoji, or emoji only, in a between-subjects design. Like that previously seen for hand gesture, emoji improved comprehension. Participants were more likely to correctly interpret indirect speech in the emoji-only condition compared with the text+emoji and the text-only conditions, and more likely in the text+emoji condition compared to the text-only condition. Thus, emoji are not mere decoration, but rather are integrated with text to communicate and disambiguate complex messages. Similar to gesture in face-to-face communication, emoji improve comprehension during text-based communication.
Journal Article
Emoji-SP, the Spanish emoji database: Visual complexity, familiarity, frequency of use, clarity, and emotional valence and arousal norms for 1031 emojis
This article presents subjective norms for 1031 emojis in six dimensions: visual complexity, familiarity, frequency of use, clarity, emotional valence, and emotional arousal. This is the largest normative study conducted so far that relies on subjective ratings. Unlike the few existing normative studies, which mainly comprise face emojis, here we present a wide range of emoji categories. We also examine the correlations between the dimensions assessed. Our results show that, in terms of their affective properties, emojis are analogous to other stimuli, such as words, showing the expected U-shaped relationship between valence and arousal. The relationship between affective properties and other dimensions (e.g., between valence and familiarity) is also similar to the relationship observed in words, in the sense that positively valenced emojis are more familiar than negative ones. These findings suggest that emojis are suitable stimuli for studying affective processing. Emoji-SP will be highly valuable for researchers of various fields interested in emojis, including computer science, communication, linguistics, and psychology. The full set of norms is available at:
https://osf.io/dtfjv/
.
Journal Article
Emoji Identification and Emoji Effects on Sentence Emotionality in ASD-Diagnosed Adults and Neurotypical Controls
We investigated ASD-diagnosed adults’ and neurotypical (NT) controls’ processing of emoji and emoji influence on the emotionality of otherwise-neutral sentences. Study 1 participants categorised emoji representing the six basic emotions using a fixed-set of emotional adjectives. Results showed that ASD-diagnosed participants’ classifications of fearful, sad, and surprised emoji were more diverse and less ‘typical’ than NT controls’ responses. Study 2 participants read emotionally-neutral sentences; half paired with sentence-final happy emoji, half with sad emoji. Participants rated sentence + emoji stimuli for emotional valence. ASD-diagnosed and NT participants rated sentences + happy emoji as equally-positive, however, ASD-diagnosed participants rated sentences + sad emoji as more-negative than NT participants. We must acknowledge differential perceptions and effects of emoji, and emoji-text inter-relationships, when working with neurodiverse stakeholders.
Journal Article
Are emojis better? The impact of generative AI emoji cues and service outcomes on user satisfaction: evidence from ERPs
To promote the sustainable development of Generative Artificial Intelligence (GenAI) applications in the service industry, enhancing user satisfaction is key. Emojis serve as catalysts for conveying emotions and enhancing user experience in online communication. However, due to the black-box nature and unpredictability of GenAI, service providers find it challenging to control the boundaries of their application. Currently, there is ongoing debate within the academic community regarding the use of emojis in GenAI, particularly concerning emotional manipulation and experience enhancement, with further exploration needed into their effectiveness and underlying mechanisms. This study is based on the emotion as social information model and employs event-related potential (ERP) technology with high temporal resolution, which is more suitable for GenAI interaction scenarios. By measuring users’ immediate cognitive processing and psychological activities, the study analyzes the underlying cognitive neural mechanisms through which emojis (presence vs. absence) and service outcomes (success vs. failure) influence user satisfaction. Behavioral results indicate that the outcome of GenAI services determines user satisfaction, while the presence or absence of emojis does not directly impact satisfaction. ERP results show that the presence of emojis compared to their absence triggers larger P3 amplitudes (emotional arousal) and N4 amplitudes (cognitive conflict); compared to service success, the presence of emojis during service failure triggers larger N4 amplitudes. This study reveals the complexity of user responses in real human-machine interaction environments, enriches research on the use of Emojis in GenAI, and provides scientific theoretical and practical foundations for GenAI design and enhancing user experience.
Journal Article