Generating a Deepfake Frame : A Text Mining Study Based on Reddit

This study investigates the understanding of deepfake, a highly realistic AI mimicry technique that is rapidly evolving to produce increasingly realistic videos and explores the construction of a deepfake framework through the lens of audience communication using framing theory. It identifies three key findings. First, the public discourse on deepfakes forms a concept hierarchy emphasising technology and its entertainment applications, with core concepts including AI, voice, actor and job, while peripheral concerns such as consent and company receive less focus. Second, employing the BERTopic algorithm, latent themes in public discussions were categorised into two dimensions: social dynamics and cultural phenomena. Third, sentiment analysis reveals predominantly neutral or negative attitudes, indicating concerns over the risks and societal impacts of deepfake technology. The deepfakes framework developed here provides a structured approach to understanding these impacts, highlighting the need for ethical considerations in technological development, regulatory measures and public education.

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Institutions

Publication Details

Journal
KOME
Published
2025-01-01
DOI
https://doi.org/10.17646/kome.of.22
Citations
1
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
3.25

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article

Generating a Deepfake Frame : A Text Mining Study Based on Reddit

Fei Qiao, Guanghui Zhou
1 citations
KOME
Spam and Phishing Detection
3.25
article

Generating a Deepfake Frame : A Text Mining Study Based on Reddit

Fei Qiao, Guanghui Zhou
article en
1 citations

Abstract

This study investigates the understanding of deepfake, a highly realistic AI mimicry technique that is rapidly evolving to produce increasingly realistic videos and explores the construction of a deepfake framework through the lens of audience communication using framing theory. It identifies three key findings. First, the public discourse on deepfakes forms a concept hierarchy emphasising technology and its entertainment applications, with core concepts including AI, voice, actor and job, while peripheral concerns such as consent and company receive less focus. Second, employing the BERTopic algorithm, latent themes in public discussions were categorised into two dimensions: social dynamics and cultural phenomena. Third, sentiment analysis reveals predominantly neutral or negative attitudes, indicating concerns over the risks and societal impacts of deepfake technology. The deepfakes framework developed here provides a structured approach to understanding these impacts, highlighting the need for ethical considerations in technological development, regulatory measures and public education.

KOMEVol. 13(1)
Guangdong University of Foreign Studies (CN)
Basic and Applied Basic Research Foundation of Guangdong Province
Quality Education
Openalex Percentile: Top 12%
Spam and Phishing Detection
3.25
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Generating a Deepfake Frame : A Text Mining Study Based on Reddit — Fei Qiao, Guanghui Zhou · KOME (2025) | TGRS Research Map | TGRS