Dubbing and reception: The revival of Chinese dubbese as internet memes on social media

Chinese dubbese, a stylised register of Mandarin used in dubbed audiovisual content, originated in the pre-digital era but has recently undergone an unexpected revival on social media platforms. While existing research has focused on dubbese’s formal linguistic features, this study shifts the focus to its circulation, reception, and various functions in participatory digital culture. Drawing on a curated corpus of highly viewed dubbese-themed videos and user comments from Bilibili, China’s most popular video-sharing site, this article uses a qualitative analysis to examine how online audiences reappropriate dubbese as a memetic and performative linguistic resource. The findings challenge the prevailing assumption in audiovisual translation studies that audiences prioritise linguistic naturalness in dubbing. Instead, users embrace dubbese for its stylised foreignness, nostalgic resonance, and playful aesthetic, transforming it into a source of affective engagement and internet humour. Dubbese expressions function as both reusable phrasal templates and widely circulated catchphrases, revealing how language from dubbed media is revitalised as internet memes. By situating dubbese within the framework of internet memes and online subcultures, this study contributes to ongoing debates in audiovisual translation studies, and calls for a reassessment of how dubbed language operates in contemporary digital life.

Authors

Publication Details

Journal
The Journal of Specialised Translation
Published
2025-07-30
DOI
https://doi.org/10.26034/cm.jostrans.2025.8470
Primary Topic
Translation Studies and Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dubbing and reception: The revival of Chinese dubbese as internet memes on social media

Xuemei Chen
The Journal of Specialised Translation
Translation Studies and Practices
article

Dubbing and reception: The revival of Chinese dubbese as internet memes on social media

Xuemei Chen
article en

Abstract

Chinese dubbese, a stylised register of Mandarin used in dubbed audiovisual content, originated in the pre-digital era but has recently undergone an unexpected revival on social media platforms. While existing research has focused on dubbese’s formal linguistic features, this study shifts the focus to its circulation, reception, and various functions in participatory digital culture. Drawing on a curated corpus of highly viewed dubbese-themed videos and user comments from Bilibili, China’s most popular video-sharing site, this article uses a qualitative analysis to examine how online audiences reappropriate dubbese as a memetic and performative linguistic resource. The findings challenge the prevailing assumption in audiovisual translation studies that audiences prioritise linguistic naturalness in dubbing. Instead, users embrace dubbese for its stylised foreignness, nostalgic resonance, and playful aesthetic, transforming it into a source of affective engagement and internet humour. Dubbese expressions function as both reusable phrasal templates and widely circulated catchphrases, revealing how language from dubbed media is revitalised as internet memes. By situating dubbese within the framework of internet memes and online subcultures, this study contributes to ongoing debates in audiovisual translation studies, and calls for a reassessment of how dubbed language operates in contemporary digital life.

The Journal of Specialised Translation(44)
Openalex Percentile: Top 71%
Translation Studies and Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.