Influence of False Self-Presentation on Mental Health and Deleting Behavior on Instagram: The Mediating Role of Perceived Popularity

Feeling phony online - The impostor phenomenon's link to online self-presentation, self-esteem, and social network site use

This study examined the relationship between the impostor phenomenon, self-esteem, online self-presentation, and social network site usage across Instagram, Facebook, TikTok, LinkedIn, and Tinder. The sample consisted of n = 541 individuals (46 % men; Mage = 27, SDage = 8.5). In addition to demographics, quantitative social network site use metrics, and self-esteem, the impostor expression emerged as an incremental predictor for the online self-presentation facets adaptable self (β = 0.26, p < .001), authentic self (β = -0.29, p < .001) and freedom of self online (β = 0.25, p < .001). Self-esteem partially mediated the impostor expression's effect on the authentic self. Findings suggest that a high impostor expression links to lower online authenticity, higher online self-adaption, and a preference for online communication and self-presentation.

Changing to Impress: Testing a Mediation Model from Instagram Self-presentation to Cosmetic Surgery

BACKGROUND

This study examined the relationship between false self-presentation on Instagram and consideration of cosmetic surgery through the mediating role of body image control in photos (BICP), photo manipulation, and body shame. We predicted that false self-presentation on Instagram was indirectly associated with cosmetic surgery intentions through the aforementioned constructs.

METHODS

A total of 504 young Italian adults (28.2% males, 18-30 years) completed an online survey. They completed a questionnaire containing the Self-presentation on Instagram Questionnaire, the Body Image Control in Photos Questionnaire-revised, the Photo Manipulation Scale, the Objectified Body Consciousness Scale, and the Acceptance of Cosmetic Surgery Scale. The pattern of associations between the constructs was analyzed via path analysis.

RESULTS

The results show that false self-presentation on Instagram was associated with photo manipulation, both directly and indirectly, through BICP. Furthermore, photo manipulation was linked to body shame, but neither of them was associated with cosmetic surgery intentions. Finally, false self-presentation on Instagram was associated with the consideration of cosmetic surgery only through the mediation of BICP.

CONCLUSION

Findings indicate that self-presentation styles might affect Instagram photo behaviors and individuals' cosmetic surgery intentions, suggesting that surgeons should fully examine patients' motivations before providing them with services. Furthermore, intervention programs encouraging users to present a more authentic version of themselves online might reduce the risk of self-objectification and reduce the consideration of procedures aimed at modifying one's body for purely aesthetic reasons.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

The dimensional structure of the Camouflaging Autistic Traits Questionnaire (CAT-Q) and predictors of camouflaging in a representative general population sample

OBJECTIVES

Some autistic people "camouflage" their differences by modeling neurotypical behaviors to survive in a neurotypical-dominant social world. It remains elusive whether camouflaging is unique to autism or if it entails similar experiences across human groups as part of ubiquitous impression management (IM). Here we examined camouflaging engagement and theoretical drivers in the general population, drawing on the transactional IM framework and contextualizing findings within both contemporary autism research and the past IM literature.

METHODS

A large representative U.S. general population sample (N = 972) completed this survey study. We combined exploratory item factor analysis and graph analysis to triangulate the dimensional structure of the Camouflaging Autistic Traits Questionnaire (CAT-Q) and examined its correspondence with prior autism-enriched psychometric findings. We then employed hierarchical regression and elastic-net regression to identify the predictors of camouflaging, including demographic (e.g., age, gender), neurodivergence (i.e., autistic and ADHD traits), socio-motivational, and cognitive factors.

RESULTS

We found a three-factor/dimensional structure of the CAT-Q in the general population, nearly identical to that found in previous autism-enriched samples. Significant socio-motivational predictors of camouflaging included greater social comparison, greater public self-consciousness, greater internalized social stigma, and greater social anxiety. These camouflaging drivers overlap with findings in recent autistic camouflaging studies and prior IM research.

CONCLUSIONS

The novel psychometric and socio-motivational evidence demonstrates camouflaging as a shared social coping experience across the general population, including autistic people. This continuity guides a clearer understanding of camouflaging and has key implications for autism scholars, clinicians, and the broader clinical intersecting with social psychology research. Future research areas are mapped to elucidate how camouflaging/IM manifests and functions within person-environment transactions across social-identity and clinical groups.

Autism, Discrimination and Masking: Disrupting a Recipe for Trauma

Outcomes of discrimination for autistic people include masking, social isolation and exclusion, trauma, and mental health problems. This discursive paper discusses the discrimination experienced by autistic people in health, education, employment, and media settings; and considers how to reduce the impact of discrimination. Addressing the impact of discrimination requires an improved understanding of the lived experience of the autistic person, the embedding of inclusive practices in communities, developing the person's life skills and supports, and enabling the person to work through their psychological trauma. Further research can aid in minimizing and preventing discrimination against people with autism.

A scoping review of social media in child, adolescents and young adults: research findings in depression, anxiety and other clinical challenges

BACKGROUND

Social media and other technologies are reshaping communication and health.

AIMS

This review addresses the relationship between social media use, behavioural health conditions and psychological well-being for youth aged <25 years.

METHOD

A scoping review of 11 literature databases from 2000 to 2020 explored research studies in youth in five areas: clinical depression and anxiety, quantitative use, social media mode, engagement and qualitative dimensions and health and well-being.

RESULTS

Out of 2820 potential literature references, 140 met the inclusion criteria. The foci were clinical depression and anxiety disorders (n = 78), clinical challenges (e.g. suicidal ideation, cyberbullying) (n = 34) and psychological well-being (n = 28). Most studies focused on Facebook, Twitter, Instagram and YouTube. Few studies are longitudinal in design (n = 26), had comparison groups (n = 27), were randomised controlled trials (n = 3) or used structured assessments (n = 4). Few focused on different youth and sociodemographic populations, particularly for low-income, equity-seeking and deserving populations. Studies examined association (n = 120; 85.7%), mediating (n = 16; 11.4%) and causal (n = 4; 2.9%) relationships. Prospective, longitudinal studies of depression and anxiety appear to indicate that shorter use (≤3 h/day) and purposeful engagement is associated with better mood and psychological well-being. Depression may predict social media use and reduce perception of support. Findings provide families, teachers and providers ways to engage youth.

CONCLUSIONS

Research opportunities include clinical outcomes from functional perspective on a health continuum, diverse youth and sociodemographic populations, methodology, intervention and privacy issues. More longitudinal studies, comparison designs and effectiveness approaches are also needed. Health systems face clinical, training and professional development challenges.

Supervised machine learning models for depression sentiment analysis

Introduction

Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques to predict the level of depression earlier in social media users' posts.

Methods

The datasets used in this research were obtained from Twitter posts. Four machine learning models, namely extreme gradient boost (XGB) Classifier, Random Forest, Logistic Regression, and support vector machine (SVM), were employed for the prediction task.

Results

The SVM and Logistic Regression models yielded the most accurate results when applied to the provided datasets. However, the Logistic Regression model exhibited a slightly higher level of accuracy compared to SVM. Importantly, the logistic regression model demonstrated the advantage of requiring less execution time.

Discussion

The findings of this study highlight the potential of utilizing machine learning models and sentiment analysis techniques for early detection of depression in social media users. The effectiveness of SVM and Logistic Regression models, with Logistic Regression being more efficient in terms of execution time, suggests their suitability for practical implementation in real-world scenarios.