Beauty Filters for Physical Attractiveness: Idealized Appearance and Imagery, Visual Content and Representations, and Negative Behaviors and Sentiments
Beauty Filters for Physical Attractiveness: Idealized Appearance and Imagery, Visual Content and Representations, and Negative Behaviors and Sentiments

Summary/Abstract: This paper provides a systematic literature review of studies investigating image and body beautification tools and deep learning-based facial retouching in relation to digitally networked images. The analysis highlights that photo editing and beauty apps configure unrealistically ideal body images in terms of normative standards. Throughout April 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “beauty filters for physical attractiveness” + “idealized appearance and imagery,” “visual content and representations,” and “negative behaviors and sentiments.” As we inspected research published between 2017 and 2022, only 168 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 24, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.

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