Internet Pragmatics - Online First

In this study, we examine the online stories of migrants, refugees, and asylum seekers curated by UK-based charity organisations. Through a mixed-methods approach, we focus on the online emergence of a positive storytelling canon that privileges certain affective positions over others. These positions feed into a problematic sense-making about migration in the UK that celebrates the stories of those migrants who incur positive (affective) changes in their lives, at the expense of potentially marginalizing others. Using a critical storytelling perspective that draws on the frameworks of affective positioning and small stories, we take one representative story as our focus to illustrate the affective participation frameworks made available to migrants. We discuss the linguistic and multimodal configuration of the story online, and the implications of the affective positions created for migrants for wider discourses about migration in the UK. Finally, we show how certain exemplary stories are static and affect-centred and do not seem to capitalise on dialogic and interactive participatory formats, unlike other forms of online storytelling. We argue that they contribute to a positive migrant storytelling canon that is well established in advocacy contexts and may have overtly empowering but covertly disempowering effects.

This paper focuses on the use of three visually presented frames — Lungs, Twin Towers, and Ice Cream — across a range of multimodal, public-facing, broadly ‘persuasive’ discourse (including advertising and online campaigning). We focus on metonymic frame evocation as the primary mechanism by which these multimodal artefacts prompt affective responses, with a special focus on evocations by means of minimalist, schematized visual representations of the chosen frame, reducing the level of detail involved but strengthening affective impact (we refer to this phenomenon as affective enrichment). In the final section, we focus on examples of ads or campaigns (involving, again, the frames of Lungs, Twin Towers or Ice Cream) in which the frame evocation is unsuccessful and ineffective, with visual overload and persuasive bleaching (Dancygier 2023) producing conflicting or unwelcome persuasive and affective inferences. Overall, our analysis of schematization and affective enrichment presents an alternative meaning-making strategy to the process of image-schematic scaffolding (Dancygier and Vandelanotte 2017) in multimodal discourse, set out in earlier work.

This study examines the pragmatics of affect and emotion in asynchronous eating shows, a genre of digital food discourse in which hosts record and share videos of themselves eating, with viewer interaction occurring through deferred comments. While previous research has explored food evaluation as a linguistic and multimodal practice, we argue that in eating shows, taste assessment is also an affective practice that fosters intimacy, engagement, and participation. Through a corpus-assisted discourse analysis of five YouTube eating shows, we investigate how hosts perform affective stances toward food using linguistic, embodied, and audiovisual resources. We also analyze how viewers, despite the asynchronous format, contribute to the co-construction of affect through patterns of evaluative commentary. Our findings show that food assessment in eating shows is not only about describing taste but also about performing and eliciting affective alignment, shaping digital food identities such as expert eaters and lay reviewers.

For several years, social media users have compared TikTok and the defunct short-video platform Vine through references to specific Vine videos and by attributing “Vine energy” to Vine-like tiktoks. Based on qualitative coding of 201 tiktoks and analysis of their comments, this article examines both discourse practices. I analyze how TikTok users create intertextual references to Vine and offer a taxonomy of communicative modes and platform features employed for multimodal cross-platform intertextual reference. I identify the semiotic features shared by tiktoks with “Vine energy,” which I argue constitutes a distinct short-form genre, and demonstrate that they are based on a partial collective memory of Vine. Using the framework of media(ted) nostalgia, I argue that fans’ positively biased participatory media texts have indelibly shaped how Vine is remembered on TikTok. Through my analysis I show that vine references and “Vine energy” comments both index “knowledgeable fan” identity and create affiliation between TikTok users through expression and recognition of shared knowledge.

This paper examines how emoji operate as pragmatic resources for negotiating affiliation in TikTok comment threads. Adopting a social semiotic perspective and the emoji–text convergence model (Zappavigna and Logi 2024a), the study analyses how emoji interact with their co-text to frame stance, modulate affect, and index shared or contested values. Combining concordance-based and interactional analyses, we show how emoji guide interpretation and shape alignment in participatory comment cultures. Emoji such as the Skull 💀 and Face With Rolling Eyes 🙄 are used to intensify mockery and disalignment, while Hot Beverage ☕ and Red Heart ❤ foster conviviality and in-group solidarity. These findings highlight that emoji meanings are inherently contextual and multimodal, emerging through their coordination with linguistic resources. By foregrounding pragmatic functions such as irony, mock politeness, and affective intensification, the study demonstrates how emoji not only manage alignment but also enact classed orientations to taste and civility. These are socially stratified preferences and norms in digital discourse around what counts as legitimate or refined behaviour regarding, for example, social practices such as coffee consumption. In doing so, the study extends prior typologies of emoji function (e.g., Herring, Dainas and Tang 2021) and models of emoji–text relations (Zappavigna and Logi 2024a) by showing how emoji perform ideological work in participatory discourse about everyday commodities.

While emojis have been widely studied in digital discourse, their role in specific speech acts like compliments remains understudied. This study examines 1,000 emoji-containing compliments from TikTok (English) and DouYin (Chinese), aiming to classify emoji pragmatic functions and usage patterns. A refined taxonomy is developed, including three higher-level functions, eleven sub-functions, four positional types, and four co-occurrence types. Findings reveal significant cross-linguistic and platform-based differences. The thumbs-up emoji (👍) is dominant in Chinese compliments, while the smiling face with hearts (🥰) leads in English. English users tend to use emojis to express emotions, whereas Chinese users more often use them to stylize the message or simulate gestures. Although emojis typically occur in sentence-final position, Chinese users show a higher degree of repetition compared to English users. This study offers a corpus-based, cross-linguistic analysis of emoji functions in compliments, proposing a refined functional taxonomy and revealing multimodal pragmatic patterns across platforms and languages.

There has been an explosion in social media use, with Statista estimating that worldwide, Facebook has over 3 billion regular active users, YouTube 2.5 billion, and Instagram and WhatsApp 2 billion (Statista 2023). While social media allows one to connect and interact with a range of people, increased social media use can be associated with feelings of isolation and symptoms of depression and anxiety. This may in part be because it allows users to engage in activities that appear social but that do not provide meaningful social interaction. We developed the Conversationality Index to assess the quality of social media exchanges based on the length, number of participants and how equally the participants contribute to a written online conversation. After calibrating the Conversationality Index using real and surrogate data, we assessed conversations taken from a 33-million-word database and found that the Conversationality Index consistently distinguished between conversations of varying quality.