Metaphor Translation as Reframing: Chinese Versus Western Stance Mediation in COVID-19 News Reports

Liu, Yufeng; Li, Dechao

doi:10.1007/978-981-19-6680-4_2

Cited by 4 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…de Saint et al, 2021 , Puglisi, 2011 , Liu and Li, 2022 , Liu and Li, 2023 , Tay, 2017 , Tay, 2019 , Tay, 2021a , Tay, 2021b , Tay, 2022a , Tay, 2022b .…”

Section: Uncited Referencesmentioning

confidence: 98%

Modelability of WAR metaphors across time in cross-national COVID-19 news translation: An insight into ideology manipulation

Liu

Tay

2023

Lingua

View full text Add to dashboard Cite

“…de Saint et al, 2021 , Puglisi, 2011 , Liu and Li, 2022 , Liu and Li, 2023 , Tay, 2017 , Tay, 2019 , Tay, 2021a , Tay, 2021b , Tay, 2022a , Tay, 2022b .…”

Section: Uncited Referencesmentioning

confidence: 98%

Modelability of WAR metaphors across time in cross-national COVID-19 news translation: An insight into ideology manipulation

Liu

Tay

2023

Lingua

View full text Add to dashboard Cite

“…Some findings indicate that the Chinese government predominantly employed war metaphors, organism metaphors, and architectural metaphors in its campaign against the pandemic ( Gui, 2021 ). Moreover, some scholars have scrutinized the metaphorical discourse surrounding the novel coronavirus in Western media from the perspective of CMA ( Durgun et al, 2022 ; Alkhammash, 2023 ; Liu and Li, 2023 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing COVID-19 metaphors in Chinese and English social media with critical metaphor analysis

2023

Front. Psychol.

View full text Add to dashboard Cite

Metaphors extracted from COVID-19-related online texts offer a unique lens for examining how individuals perceive the pandemic. Users from distinct linguistic backgrounds may select varying source domains to discuss COVID-19, with these choices influenced by multiple factors. Utilizing Critical Metaphor Analysis (CMA) theory and employing the Metaphor Identification Procedure VU (MIPVU), this study conducts a comparative analysis of Chinese and English COVID-19-related metaphors derived from social media platforms, specifically Twitter and Weibo. The findings reveal both commonalities and distinctions between the metaphors employed in Chinese and English texts. Commonalities encompass the widespread use of war and disaster metaphors in both sets of texts. Distinctions are characterized by a higher prevalence of zombie metaphors in English texts and classroom metaphors in Chinese texts. These similarities and differences can be attributed to varying socio-historical factors, as well as the active choices of users to express their values and judgments.

show abstract

“…Different attributes and methods have been suggested for the task of detecting stance, both on the post and user levels, such as lexicon and deep neural text representations [7,11,12], or user interaction and network information [2,13]. Most of the studies on stance detection have been carried out on speci c countries [14][15][16][17][18], certain user communities [19][20][21] or speci c text languages [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Diverse Enough but with Common Views:Building a Global Stance Classifier on COVID-19

Benkhedda,

Magdy

2024

Preprint

View full text Add to dashboard Cite

Stance detection, which determines a user’s position on a specific topic through their generated content or interactions, has been widely studied for various domains. However, most existing work focuses on regional or community-specific topics, lacking a global perspective. In this paper, we investigate the ability to detect stance on the COVID-19 pandemic, a truly global issue transcending geographical and cultural boundaries. We compile a large, multilingual dataset of 7.9 million tweets related to COVID-19, accompanied by media content, spanning 3,516 users from 90 countries and 31 languages. Our objective is to develop an effective stance detection approach that can accurately predict users’ stances (pro-vax or anti-vax) regardless of their language or location. To achieve this, we propose a network-based method that leverages user interactions on Twitter, such as friends, likes, replies, and mentions, in addition to textual content. Despite the significant cultural diversity within our dataset, our approach demonstrates the ability to accurately predict users’ COVID-19 stance by analyzing their interaction signals and network homophily patterns. Our classification model achieves an F-score of 0.95 for both pro-vax and antivax user stances, surpassing state-of-the-art text-based methods. The findings suggest that echo-chamber effects and network homophily can extend beyond borders and languages, forming global patterns of polarization around certain topics. Our work highlights the potential of network-based approaches for stance detection on global issues and contributes insights into the challenges and opportunities of developing inclusive and robust models across diverse contexts.

show abstract

Metaphor Translation as Reframing: Chinese Versus Western Stance Mediation in COVID-19 News Reports

Cited by 4 publications

References 44 publications

Modelability of WAR metaphors across time in cross-national COVID-19 news translation: An insight into ideology manipulation

Modelability of WAR metaphors across time in cross-national COVID-19 news translation: An insight into ideology manipulation

Comparing COVID-19 metaphors in Chinese and English social media with critical metaphor analysis

Diverse Enough but with Common Views:Building a Global Stance Classifier on COVID-19

Contact Info

Product

Resources

About