Racial color blindness: a corpus-assisted discourse analysis of social media reactions to Disney's Little Mermaid teaser
Social media, as an interactive platform, reflects society's reality. This study explored how racial color blindness ideology is reinforced and reshaped through users' discourse patterns on social media platforms. It refers to the ongoing process through which individuals collectively...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
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| Online Access: | http://journalarticle.ukm.my/26274/1/Gema_Online_25_3_3.pdf http://journalarticle.ukm.my/26274/ https://ejournal.ukm.my/gema/issue/view/1852 |
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| Summary: | Social media, as an interactive platform, reflects society's reality. This study explored how racial
color blindness ideology is reinforced and reshaped through users' discourse patterns on social
media platforms. It refers to the ongoing process through which individuals collectively redefine,
negotiate, and perpetuate color-blind ideologies through their digital interactions and responses to
racially charged media content. This research specifically aimed to identify linguistic patterns and
discourse strategies that demonstrate how social media users' responses to Disney's The Little
Mermaid teaser contribute to the maintenance and transformation of racial color blindness
ideology in contemporary digital spaces. Racial color blindness persists as a critical issue in
contemporary media representation, with this study investigating how social media users construct
and negotiate racial discourse through their responses to Disney's casting decisions in The Little
Mermaid teaser. Utilizing a Corpus-Assisted Discourse Studies (CADS) approach, the research
analyzed 75,000 YouTube comments (948,610 tokens) for quantitative corpus analysis and
supplementary Twitter (hereinafter referred to as X) data for qualitative discourse interpretation
using Stuart Hall's encoding-decoding theory. Data collection involved multi-platform scraping
through the YouTube API and X data collection, with corpus processing through AntConc
software, integrating quantitative corpus linguistic techniques with qualitative discourse analysis.
The multi-platform analysis revealed three primary response categories: dominant (embracing
diversity), negotiated (ambivalent representation), and oppositional (rejecting racial recasting). |
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