Internet Pragmatics - Online First

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.