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

Kusci I., Bakir V., ARPACI İ.

ONLINE INFORMATION REVIEW, 2026 (SCI-Expanded, SSCI, Scopus) identifier

  • Publication Type: Article / Article
  • Publication Date: 2026
  • Doi Number: 10.1108/oir-02-2025-0115
  • Journal Name: ONLINE INFORMATION REVIEW
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Compendex, Education Abstracts, Information Science and Technology Abstracts, INSPEC, Library, Information Science & Technology Abstracts (LISTA), Psicodoc, vLex, DIALNET
  • Bursa Uludag University Affiliated: Yes

Abstract

PurposeThis 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/approachThis 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.FindingsPsychometric 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 (alpha = 0.908) and satisfactory construct validity, ensuring the reliability of our findings.Originality/valueThe 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 reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2025-0115