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Modelling Emoji–Text Relations

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Abstract

This chapter introduces the framework for exploring emoji-text relations in social media that is used in this book. The chapter begins by explaining the discourse semantic systems that have been developed in Systemic Functional Linguistics for describing ideational, interpersonal, and textual meaning. This is in order to lay the foundation for exploring the linguistic meanings with which emoji coordinate in subsequent chapters. The chapter then introduces the concept of ‘intermodal convergence’ used in social semiotics to describe how semiotic modes such as language and images coordinate to make meaning. The chapter outlines the principles that we use for determining emoji-text convergence, including proximity, minimum mapping, and prosodic correspondence. It concludes with an overview of the system of emoji-text convergence, presenting the system network guiding the close textual analysis conducted on the social media corpora used in the book.

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From Twitter to Reddit, Facebook, and WhatsApp – social media is a part of modern everyday life. Studying the language used on social media platforms presents great opportunities as well as challenges to corpus linguists. The contributions in Corpus Approaches to Social Media address technical, ethical, and methodological issues by showcasing in-depth social media studies as conducted by corpus scholars. The chapters are based on a variety of social media platforms and include corpus perspectives on the language of online communities, linguistic variation in short media texts, and the role of images in computer-mediated communication. A particularly strong point of the collection are the detailed accounts of the methodological aspects of working with social media corpora. The volume features research applying traditional corpus linguistic methods to social media data as well as novel and innovative research methods for the analysis of multimodal material and atypical corpus texts.
Article
This paper explores the pragmatics of emojis co-occurring with or embedded in text on Chinese social media with this central research question: what are the patterns and the communicative functions manifested by emojis in co-occurrence with Chinese text? Building on the metafunctional approach of multimodal analysis, popular online posts from Sina Weibo which contain both emoji(s) and text have been collected and analyzed to discover the representational, interactive, and compositional features manifested by emojis co-occurring with text. We have found that these emojis on Weibo appear most frequently at the end of the posts and reflect some unique Chinese cultural and linguistic features. Based on recurring pragmatic and functional patterns of text-emoji co-occurrences, it is proposed that emojis are used to perform speech acts, highlight subjective interpretations, and enhance informality, while substituting, reinforcing, and complementing the meanings conveyed by verbal language.
Article
This study posits that graphicon use follows an evolutionary trajectory characterized by stages. Drawing on evidence that the uses and functions of emoticons have changed over time and that the introduction of emoji affected the popularity and usage of emoticons, we examine the uses of the newer types, emoji and stickers, and consider the relationship of stickers to emoji. Adapting the apparent-time method from the sociolinguistic study of language change, we compare sticker and emoji use by English-speaking Facebook Messenger users, exploring how they are used and under what conditions using semi-structured interviews and a large-scale survey. Stickers are argued to be more pragmatically marked for emotional intensity, positivity, and intimacy, characteristic of a more recent stage of evolution, while emoji use exhibits signs of conventionalization and pragmatic unmarking. The identification of patterns that characterize evolutionary stages has implications for future graphicon use.
Article
This paper is a large-scale quantitative examination of the conversational use of nine English Typed Laughter-Derived Expressions (TLDEs) on Twitter: haha, hehe, tehe, lol, lmao, lmfao, (Unicode: 1f602), (1f923), and (1f606). The paper aims to establish a quantitative groundwork capable of supporting future qualitative research attempting to answer questions about how and why TLDEs are used in digital conversation on various platforms and about how our use of TLDEs is related to our use of physical laughter in face-to-face exchanges. The analyses conducted examine various conversation distributional characteristics of TLDEs in Twitter conversations. One analysis examines the placement of TLDEs within tweets (initial, medial, final, alone), concluding that while all TLDEs are preferentially used in tweet-final position, this trend is much stronger for emoji forms than letter-based forms. Another analysis concluded that TLDEs are disproportionately found in tweets with high levels of hearer/reader ratification. Another found that initial-position TLDEs are more likely to be used in contexts which permit reference to a laughable in previous utterances than are medial- or final-position TLDEs. Finally, this study found that the offer/acceptance pattern of spoken laughter is weak to nonexistent in Twitter exchanges.
Article
An increasingly large body of converging evidence supports the idea that the semantic system is distributed across brain areas and that the information encoded therein is multimodal. Within this framework, feature norms are typically used to operationalize the various parts of meaning that contribute to define the distributed nature of conceptual representations. However, such features are typically collected as verbal strings, elicited from participants in experimental settings. If the semantic system is not only distributed (across features) but also multimodal, a cognitively sound theory of semantic representations should take into account different modalities in which feature-based representations are generated, because not all the relevant semantic information may be easily verbalized into classic feature norms, and different types of concepts (e.g., abstract vs. concrete concepts) may consist of different configurations of non-verbal features. In this paper we acknowledge the multimodal nature of conceptual representations and we propose a novel way of collecting non-verbal semantic features. In a crowdsourcing task we asked participants to use emoji to provide semantic representations for a sample of 300 English nouns referring to abstract and concrete concepts, which account for (machine readable) visual features. In a formal content analysis with multiple annotators we then classified the cognitive strategies used by the participants to represent conceptual content through emoji. The main results of our analyses show that abstract (vs. concrete) concepts are characterized by representations that: 1. consist of a larger number of emoji; 2. include more face emoji (expressing emotions); 3. are less stable and less shared among users; 4. use representation strategies based on figurative operations (e.g., metaphors) and strategies that exploit linguistic information (e.g. rebus); 5. correlate less well with the semantic representations emerging from classic features listed through verbal strings.
Article
This article explores the role of Bitzer's constraint through Brooke's ecologies of code, culture, and practice as a method to analyze emoji as a form of multimodal public rhetoric. In particular, this article suggests that understanding digital forms such as emoji, which are controlled by the Unicode consortium, requires understanding the essential role that code literacy can play in shaping public writing practices in social media through two case studies: one on the multicultural skin tone emoji iOS update of 2015 and another on public emoji proposals as a way to illustrate how everyday rhetors can use networked writing intervene at the level of code.
Book
Cambridge Core - Media, Mass Communication - The Emoji Revolution - by Philip Seargeant