THEORETICAL BACKGROUND

The phenomenon of social media appearance preoccupation can be understood through two theoretical frameworks outlined in the literature, each addressing the question of who benefits from social media use in terms of social capital. These frameworks present opposing hypotheses regarding who benefits from social media use in terms of social capital. The rich-get-richer hypothesis suggests that socially affluent people, characterized by high levels of friendship support or low loneliness, are more likely to benefit from social media use (Cheng et al., 2019; Desjarlais & Willoughby, 2020). This is because their existing positive relationships with friends are reinforced and strengthened through online interactions (Kraut et al., 2002; Valkenburg & Peter, 2007a, b). This perspective posits that individuals with wellness and already well-established social networks, marked by high levels of friendship support or lower levels of loneliness, leverage social media to enhance their existing positive relationships, leading to increased social capital. This could be attributed to a variety of factors, such as the desire to maintain their status and reputation within their social circles, or the pressure to conform to societal beauty standards prevalent on these platforms. Additionally, individuals with high levels of friendship support or lower levels of loneliness may still experience insecurities related to their appearance, which could drive them to seek validation and affirmation through social media interactions (Desjarlais & Willoughby, 2020; Pouwels et al., 2021). Conversely, the poor-get-richer hypothesis suggests that socially impoverished individuals, characterized by low friendship support or high loneliness, may benefit more from using social media. In this scenario, social media use serves as a compensatory mechanism, helping these individuals overcome the challenges of loneliness and lack of offline friendship support (Pouwels et al., 2021). This hypothesis proposes that individuals facing social deficits or insufficient offline social connections may find solace and social interaction through online platforms, compensating for their perceived inadequacies in face-to-face interactions (Hjetland et al., 2022; McKenna & Bargh, 2000; Sheldon, 2008).

In the present study, we extend these frameworks by incorporating additional indicators such as self-esteem, well-being, anxiety, depression, appearance-related anxiety, and leisure hours of media use. These extensions are grounded in their relevance to understanding the phenomenon of SMAP and its association with social capital. These indicators have been extensively studied in the literature and are widely recognized as crucial components of individuals’ psychological well-being and social functioning.

Self-esteem, which refers to individuals’ overall evaluation of their own worth or value, is a fundamental aspect of psychological well-being consistently linked to various outcomes, including social behavior and adjustment (Rosenberg, 1965). Well-being encompasses individuals’ overall satisfaction with their lives, including aspects such as happiness, fulfillment, and contentment, serving as a broader indicator of psychological health and functioning (Diener et al., 1985). Anxiety reflects individuals’ emotional state characterized by feelings of apprehension, worry, or nervousness, playing a significant role in shaping individuals’ experiences in social contexts and influencing their engagement with social media platforms (Barlow, 2002). Depression refers to a mood disorder characterized by persistent feelings of sadness, hopelessness, and loss of interest or pleasure in activities, with profound implications for individuals’ social interactions and overall functioning (American Psychiatric Association, 2013). Appearance-related anxiety specifically pertains to individuals’ concerns and apprehensions regarding their physical appearance, particularly relevant in the context of social media where appearance-related comparisons and evaluations are prevalent (Cash et al., 2004). Leisure hours of media use also play a significant role in understanding social media use patterns and their impact on individuals’ psychological well-being and social functioning (Hidalgo-Andrade et al., 2021; Iannizzotto et al., 2020; Rowe, 2006). This variable can serve as a compensatory mechanism for individuals, helping to alleviate feelings of loneliness and providing opportunities for social interaction that may be lacking offline. Additionally, it can influence the development of social connections and support networks, contributing to the enhancement of social capital among individuals who may initially have fewer resources in traditional offline social settings.

The novelty of this research lies in its comprehensive investigation into the phenomenon of social media appearance preoccupation and its intricate relationship with various psychosocial factors.

AIM OF THE PRESENT STUDY

The present study aims to contribute to the theoretical understanding of SMAP by exploring its association with various relevant variables, including self-esteem, well-being, appearance-related anxiety, anxiety, depression, and leisure hours of media use.

In line with the rich-get-richer hypothesis, we hypothesize that individuals with higher levels of self-esteem, well-being, and lower levels of appearance-related anxiety, anxiety, depression, and leisure hours of media use will demonstrate a positive association with SMAP. This hypothesis suggests that socially affluent individuals may further enhance their psychological well-being and self-esteem through increased engagement with social media platforms.

Conversely, in line with the poor-get-richer hypothesis, we hypothesize that individuals with lower levels of self-esteem and well-being, and higher levels of appearance-related anxiety, State-Trait Anxiety Inventory score, Beck Depression Inventory score, and leisure hours of media use will also exhibit a positive association with SMAP. This hypothesis proposes that socially impoverished individuals may spend more time on social media and may be more concerned about their appearance.

These hypotheses aim to provide insights into the relationship between social media use and psychological well-being, shedding light on whether the benefits derived from social media engagement are influenced by individuals’ pre-existing psychological states and the amount of leisure hours of media use.

PARTICIPANTS

The research sample comprised 401 Italian participants aged between 18 and 74 years (M = 29.09, SD = 12.18), with 58.8% identifying as female and 41.2% as male. To provide insight into the sample’s composition and socio-cultural background, the initial questionnaire section included general inquiries about the region of origin (Sicily 52%, Lombardy 48%), educational level (middle school 4%, high school diploma 50%, degree 38%, master’s degree or doctorate 5%, other 3%), and occupation (student 58%, employee 17%, unemployed 8%, homemaker 7%, entrepreneur 5%, self-employed 3%, other 2%). The survey also assessed the participants’ social media usage habits, revealing that the average number of leisure hours of media use was 3.12 (SD = 1.90), compared to 3.61 hours (SD = 2.19) spent for work or study. Instagram was the most frequently used social media platform for communication and staying updated (57.9%), followed by Facebook (21.8%), TikTok (11.9%), YouTube (7%), and Twitter (1.5%).

MEASURES

The Social Media Appearance Preoccupation Scale (SMAPS; Fabio & Tripodi, 2024; Zimmer-Gembeck et al., 2021) was employed to evaluate concerns pertaining to appearance on social media platforms. The Italian standardization of the SMAPS scale (Fabio & Tripodi, 2024), comprises 18 items distributed across three subscales: online self-presentation (seven items; M = 26.62, SD = 9.14, α = .78), appearance-related activity (five items; M = 16.81, SD = 6.73, α = .79), and appearance comparison (six items; M = 18.89, SD = 10.28, α = .90). Online self-presentation assesses the intentional curation of self-images on social media platforms. An example item is: “I carefully select the photos or videos I post to present myself in a positive light on social media”. Appearance-related activity evaluates behaviors associated with monitoring or altering one’s appearance in the online realm. For instance, respondents may indicate the frequency with which they check their appearance in photos or videos posted on social media. Appearance comparison examines tendencies to compare one’s appearance with that of others on social media platforms. An example statement from this subscale is: “I compare my appearance to that of others on social media to determine my self-worth”. Participants provided responses on a Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Subscale scores were derived by summing the responses to the respective items.

Psychological General Well-Being Index (PGWBI; Dupuy, 1990). This 7-item questionnaire measures health-related quality of life over the past four weeks. Responses were recorded on a Likert scale from 0 (strongly disagree) to 5 (strongly agree) (M = 18.19, SD = 7.86, α = .88).

Rosenberg Self-Esteem Scale (SES; Rosenberg, 1965): This scale assesses an individual’s overall self-esteem, including both positive and negative self-feelings. It consists of 10 items. An example item is: “I feel that I am a person of worth, at least on an equal plane with others”. Responses are rated on a Likert scale from 0 (not at all agree) to 3 (strongly agree) (M = 21.56, SD = 6.48, α = .89).

Appearance Anxiety Inventory (AAI; Roberts et al., 2018; Veale et al., 2014). This inventory measures anxiety symptoms related to body image and includes 10 items on a Likert scale from 0 (never) to 4 (always). Participants rated how often they experience situations described in the scale (e.g., “I compare aspects of my appearance to others”, “I am focused on how I feel I look, rather than on my surroundings”). Higher scores indicate a greater presence of symptoms related to appearance anxiety (M = 12.82, SD = 8.73, α = .87).

Beck Depression Inventory II (BDI-II; Beck et al., 1961). For this study, the original 21-item inventory was applied. The BDI-II is a widely used self-report rating scale designed to measure the severity of depression symptoms. It consists of 21 items, each rated on a 4-point Likert scale. Responses for the BDI-II assess the intensity of symptoms such as sadness, pessimism, and guilt over the past two weeks, with options ranging from 0 to 3 reflecting increasing severity. The final score is obtained by summing the responses to each item (M = 9.23, SD = 1.34, α = .88).

State-Trait Anxiety Inventory Y2 (STAI-Y2; Spielberger et al., 1983; Spielberger, 1989). The STAI is a widely used self-report rating scale designed to measure state anxiety, which refers to a transitory feeling of tension and apprehension. The STAI-Y2 consists of 20 items, each rated on a 4-point Likert scale. Responses for STAI-T assess the frequency of feelings “in general” as follows: 1 – almost never, 2 – sometimes, 3 – often, and 4 – almost always. The final score is obtained by summing the responses to each item (M = 11.2, SD = 2.34, α = .82).

To assess the participants’ social media usage habits, we administered a structured survey that included specific questions about media use for leisure and for work or study purposes. Participants were asked to report the average number of hours per day they spent on social media for each of these two categories.

The questions were designed to obtain an accurate estimate of the time spent on social media. For instance, participants were asked: “On average, how many hours per day do you spend on social media for leisure?” and “On average, how many hours per day do you use social media for work or study?”. The responses were collected on an hourly scale, allowing participants to indicate both whole and fractional hours.

PROCEDURE

Ethical approval for the study was obtained from the Ethical Committee. Participants were recruited by the authors through various social media platforms (e.g., WhatsApp, Instagram, Facebook) and were encouraged to share the study invitation with others. Participants accessed the questionnaire through a provided link. At the outset, respondents were informed about the study’s purpose, privacy and anonymity protections, and other relevant details. Only those who provided informed consent proceeded to complete the questionnaires. Participants did not receive any incentives for participation. After responding to general questions about their background, participants completed the scales. Clear instructions preceded each section of the survey, and the order of presentation was randomized. The entire survey took approximately 15 minutes to complete.

STATISTICAL ANALYSIS

Data analysis was performed using SPSS 24.0 and Amos statistical software. Descriptive statistics, including means and standard deviations, were computed for each variable. Pearson correlation analysis was conducted to examine the relationships between SMAP and the self-esteem, psychological well-being, appearance anxiety, depression, and anxiety. Bonferroni’s correction was applied for multiple comparisons. To indicate the effect size of the correlation coefficient, Cohen’s cutoffs for small (±0.10 to 0.29), medium (±0.30 to 0.49), and large effect sizes (≥ 0.50) (Cohen, 1992) were employed.

Linear regression analyses were conducted to assess the relationships between appearance anxiety, self-esteem, psychological well-being, depression, anxiety, and SMAP. Effect sizes for the regression coefficients (β) were calculated to determine the strength and direction of these relationships. R-squared values were computed to assess the proportion of variance in the dependent variable explained by the independent variables in the regression model. Furthermore, path analysis was used to determine whether positive and negative psychosocial factors had direct and indirect effects on SMAP.

LIMITATIONS

The research relied on a non-representative sample of Italian individuals, limiting the generalizability of the results to other populations or cultural contexts. Moreover, most of the variables, such as appearance-related anxiety and depression, were assessed through participants’ self-reports, which could affect the accuracy of the results due to the subjectivity of the reports.

The study used a cross-sectional design, which limits the ability to infer causal relationships between the variables examined. A longitudinal design could provide a better understanding of the temporal dynamics between social media use and psychological variables. The research relied on self-reported measures of social media use, which may be subject to biases due to lack of precision or participants’ tendency to provide socially desirable responses.

Future studies could adopt a longitudinal approach to explore the causal relationships between social media use and the psychological variables examined, allowing for a better understanding of the underlying mechanisms. Further research could also examine the impact of other factors, such as individual differences in social media use, body image perception, and coping strategies, to gain a more comprehensive understanding of social media appearance preoccupation. Comparative studies across different cultures could provide valuable insights into cultural differences in social media use and its implications for psychological well-being. Based on the study’s results, targeted interventions could be developed to promote healthy and mindful social media use and improve users’ psychological well-being.

Finally, future research should continue to explore the longitudinal and bidirectional relationships between social media use and psychological well-being, considering the dynamic nature of online interactions and their impact on individuals’ mental health outcomes.