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Framing the digital self: development and validation of the social media AI filter scale

İsmail Kuşci et al.

Online Information Review2026https://doi.org/10.1108/oir-02-2025-0115article
AJG 1ABDC B
Weight
0.37

What the paper says

Purpose This study addresses the urgent need for psychometric measurement tools to investigate the use of artificial intelligence (AI) filters on social media. The increasing prevalence of AI filters in digital self-presentation has highlighted the need for a tool to measure their use. To meet this need, we have developed and validated the Social Media AI Filter Scale (AIFS). Design/methodology/approach This study employed a systematic scale development approach, conducting a two-stage validity process using independent samples of young adults (n1 = 304, n2 = 326). Scale development was conducted in accordance with classical test theory (CTT), including expert panel reviews, structured interviews and pilot tests. Psychometric analyses included exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and measurement invariance tests. Findings Psychometric analyses revealed a 5-factor structure, accounting for 58.57% of the total variance. These factors, including social interaction and self-presentation, technological awareness and risk perception, sociocultural integration, technological self-efficacy and future orientation, provide a comprehensive understanding of AI filter usage on social media. The scale also demonstrates reliable internal consistency (α = 0.908) and satisfactory construct validity, ensuring the reliability of our findings. Originality/value The findings confirm that AIFS enhances the understanding of AI filter usage on social media by integrating the dimensions of technological acceptance, self-presentation and sociocultural impacts. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2025-0115

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https://doi.org/https://doi.org/10.1108/oir-02-2025-0115

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@article{i̇smail2026,
  title        = {{Framing the digital self: development and validation of the social media AI filter scale}},
  author       = {İsmail Kuşci et al.},
  journal      = {Online Information Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/oir-02-2025-0115},
}

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Framing the digital self: development and validation of the social media AI filter scale

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.