Developing the social media self-presentation discrepancy scale (SMSDS): psychological and social implications of self-presentation and impression management strategies

This study aimed to develop the Social Media Self-Presentation Discrepancy Scale (SMSDS) to assess individuals' tendencies to present themselves differently from their real selves in social media environments. The theoretical framework of the study was grounded in Goffman's theory of self-presentation, Higgins' self-discrepancy theory, and Leary's impression management model. Through exploratory (<i>n</i> = 243) and confirmatory (<i>n</i> = 409) factor analyses, the scale's structural integrity was established, revealing a three-factor structure comprising Reality Distortion and Exaggeration, Idealized Self-Presentation, and Strategic Impression Management. The scale demonstrated high internal consistency, and its construct validity and measurement invariance were supported through various statistical analyses. The SMSDS offers a functional assessment tool for evaluating psychological aspects of social media use, identifying risky digital behaviors among young users, and designing intervention programs targeting digital self-presentation. The findings contribute to a more comprehensive understanding of the multidimensional nature of self-presentation strategies on social media at both theoretical and applied levels.

Authors

Institutions

Publication Details

Journal
The Journal of Social Psychology
Published
2026-02-16
DOI
https://doi.org/10.1080/00224545.2026.2630716
Primary Topic
Impact of Technology on Adolescents
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Developing the social media self-presentation discrepancy scale (SMSDS): psychological and social implications of self-presentation and impression management strategies

İsmail Kuşci, Vedat Bakır
The Journal of Social Psychology
Impact of Technology on Adolescents
article

Developing the social media self-presentation discrepancy scale (SMSDS): psychological and social implications of self-presentation and impression management strategies

İsmail Kuşci, Vedat Bakır
article en

Abstract

This study aimed to develop the Social Media Self-Presentation Discrepancy Scale (SMSDS) to assess individuals' tendencies to present themselves differently from their real selves in social media environments. The theoretical framework of the study was grounded in Goffman's theory of self-presentation, Higgins' self-discrepancy theory, and Leary's impression management model. Through exploratory (<i>n</i> = 243) and confirmatory (<i>n</i> = 409) factor analyses, the scale's structural integrity was established, revealing a three-factor structure comprising Reality Distortion and Exaggeration, Idealized Self-Presentation, and Strategic Impression Management. The scale demonstrated high internal consistency, and its construct validity and measurement invariance were supported through various statistical analyses. The SMSDS offers a functional assessment tool for evaluating psychological aspects of social media use, identifying risky digital behaviors among young users, and designing intervention programs targeting digital self-presentation. The findings contribute to a more comprehensive understanding of the multidimensional nature of self-presentation strategies on social media at both theoretical and applied levels.

The Journal of Social Psychology
Bursa Uludağ Üni̇versi̇tesi̇ (TR), Kütahya Dumlupınar Üniversitesi (TR)
Reduced inequalities
Openalex Percentile: Top 59%
Impact of Technology on Adolescents
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.