Bergen Social Media Addiction Scale

Abstract

The Bergen Social Media Addiction Scale (BSMAS) is a brief, psychometrically validated screening instrument engineered to assess problematic and compulsive engagement with social networking services. Adapted directly from the earlier, platform-specific Bergen Facebook Addiction Scale (BFAS), the BSMAS embodies a foundational paradigm shift in contemporary cyberpsychology: recognizing that excessive digital socialization stems from an underlying compulsion toward the communicative and social affordances of networked technology rather than loyalty to a single commercial application. Anchored conceptually in Mark D. Griffiths’ biopsychosocial components model of behavioral addiction, the scale operationalizes six cardinal criteria of dependency: salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. The standard instrument comprises 6 self-report items evaluated on a 5-point Likert-type scale ranging from 1 (“Very rarely”) to 5 (“Very often”). The cross-cultural adaptation and psychometric validation led by Lucia Monacis, Valeria de Palo, Mark D. Griffiths, and Maria Sinatra established the structural robustness of the Italian version across a diverse cohort of adolescents and young adults (N = 734). Using robust maximum likelihood estimation (MLMV), confirmatory factor analysis verified a parsimonious unidimensional latent structure exhibiting exceptional model fit (χ²/df = 6.47, Comparative Fit Index [CFI] = 0.970, Standardized Root Mean Square Residual [SRMR] = 0.030, and Root Mean Square Error of Approximation [RMSEA] = 0.094). Advanced multigroup analyses established strict measurement invariance across gender and developmental stages (adolescents vs. young adults), proving that the instrument measures the underlying construct identically across demographic subgroups. Convergent validity, factor determinacy, and average variance extracted (AVE > 0.50) confirm the scale’s diagnostic precision, establishing the BSMAS as a gold standard screening tool for clinical evaluations and large-scale epidemiological investigations.

Keywords

Bergen Social Media Addiction Scale, behavioral addiction, social media addiction, psychometrics, confirmatory factor analysis, measurement invariance, attachment theory, biopsychosocial model, cyberpsychology, screen time assessment, problematic Internet use

Authors

The psychometric adaptation and validation of the Italian Bergen Social Media Addiction Scale was conducted by an international collaborative team of psychometricians, cyberpsychologists, and cognitive researchers:

• Lucia Monacis, Ph.D. — Department of Humanities, Letters, Cultural Heritage, and Educational Sciences, University of Foggia, Foggia, Italy. E-mail: [email protected].
• Valeria de Palo, Ph.D. — Department of Educational Sciences, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy.
• Mark D. Griffiths, Ph.D. — International Gaming Research Unit, Psychology Department, Nottingham Trent University, Nottingham, United Kingdom.
• Maria Sinatra, Ph.D. — Department of Educational Sciences, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy.

Purpose

The primary purpose of the Bergen Social Media Addiction Scale (BSMAS) is to provide researchers, clinicians, and educators with an empirically sound, psychometrically concise, and conceptually unified instrument to measure symptoms of compulsive social media usage. In the early era of social media research, measurement methodologies suffered from platform fragmentation. Early instruments focused almost exclusively on individual platforms, most notably Facebook, as exemplified by the original Bergen Facebook Addiction Scale developed by Cecilie Schou Andreassen and colleagues (2012). However, as the digital ecosystem rapidly diversified into multifaceted networks featuring photo sharing, microblogging, ephemeral messaging, and algorithmic video feeds, platform-bound scales rapidly lost external relevance. Users transitioning across applications frequently slipped through assessment cracks when their distress was tied to newer digital modalities. The BSMAS resolves this vulnerability by capturing generalized behavioral and psychological patterns across all social networking services, ensuring longitudinal clinical relevance regardless of fluctuating platform popularity.

Clinically, the BSMAS addresses the imperative to distinguish non-pathological, highly engaged digital usage from clinical behavioral addiction. In modern society, high volumes of screen time and frequent online communication often reflect mandatory occupational, academic, or normative social obligations. Overpathologizing everyday digital habits poses significant risks to diagnostic validity, as noted by critical cyberpsychology scholars (Billieux et al., 2015). The BSMAS sidesteps the confounding metric of gross screen time by evaluating functional disruption, psychological distress, loss of control, and interpersonal impairment. It identifies when digital socialization transitions from an adaptive interpersonal habit into an addictive disorder characterized by compulsive usage patterns and negative real-world outcomes.

From an epidemiological and empirical perspective, the instrument fulfills an urgent need for an ultra-brief screening battery capable of being embedded in comprehensive multivariate surveys without inflating respondent burden or precipitating participant attrition. Because it contains only six items, the scale minimizes common method bias and cognitive fatigue, making it exceptionally suited for clinical intake protocols, school-based screening initiatives, and large population-level health surveys. Furthermore, the cross-cultural validation of the BSMAS bridges vital methodological divides, permitting cross-national comparisons of digital addiction prevalence while investigating the complex etiological intersections among personality traits, emotional dysregulation, and interpersonal attachment patterns.

Psychological Construct

The Bergen Social Media Addiction Scale assesses the psychological construct of social media addiction (also termed problematic social network site use). Grounded within behavioral addiction theory, the construct is defined as an excessive, uncontrollable cognitive and behavioral preoccupation with social media platforms that leads to significant functional impairment across interpersonal, educational, occupational, and psychological domains of living. Rather than approaching addiction as a vague cluster of non-specific complaints, the BSMAS conceptualizes the phenomenon through six distinct, clinically validated core components:

• Salience: This dimension reflects the degree to which social media dominates an individual’s cognitive processes, emotional life, and behavioral choices. Cognitive salience manifests as continuous ruminative preoccupation, daydreaming about online content, or mentally rehearsing future interactions even while engaged in unrelated offline tasks. Behavioral salience occurs when the individual structures their physical routine and daily schedule primarily around acquiring or maintaining social media access.
• Mood Modification: Mood modification refers to the functional reliance on social media as an avoidant coping mechanism or a chemical-free emotion regulation strategy. Users experiencing distress, dysphoria, generalized anxiety, loneliness, or interpersonal conflict leverage the rapid dopamine rewards and social distractions of social media to alter their emotional baseline, achieving an immediate experiential “high” or tranquilizing numbness.
• Tolerance: Borrowed from classical pharmacological addiction criteria, tolerance denotes the neurobehavioral and psychological adaptation that compels the individual to spend increasing amounts of time and mental energy on social networking platforms to achieve the subjective feelings of pleasure, connection, or satisfaction that were previously elicited by brief sessions.
• Withdrawal Symptoms: This component encompasses the negative somatic, emotional, and cognitive states that emerge when social media access is abruptly severed, restricted, or unavailable. Individuals exhibit emotional lability, irritability, restlessness, acute agitation, and profound anxiety, which abate immediately upon regaining digital connectivity.
• Conflict: Conflict represents the external and internal friction engendered by uncontrolled social media involvement. External conflict encompasses severe relational strife with family members, romantic partners, and peers, alongside occupational decline or academic underachievement due to procrastination and distraction. Internal conflict refers to intrapsychic dissonance, self-reproach, and subjective distress stemming from the individual’s conscious awareness that their online habits are eroding their life priorities.
• Relapse: Relapse signifies the recurring breakdown of self-regulatory control. It is characterized by repeated, unsuccessful attempts to moderate, control, pause, or permanently cease social media usage, with the individual rapidly reverting to baseline or intensified compulsive patterns following brief periods of self-imposed restriction.

In addition to these six behavioral criteria, the validation study of the BSMAS explicitly bridges the addiction construct with attachment theory. The scale examines how internal working models of self and others drive compensatory digital socialization. Individuals displaying anxious attachment styles, who harbor chronic fears of abandonment and hyperactivating relational strategies, often utilize social media as an omnipresent channel to seek reassurance, demand social validation, and monitor peer activities. Conversely, individuals exhibiting avoidant attachment styles, who utilize deactivating strategies to suppress vulnerability and maintain emotional distance, may channel their interpersonal interactions through the protective, asynchronous barrier of digital screens. The construct captured by the BSMAS thus reflects not merely an isolated behavioral excess, but an expressive symptomatic manifestation of fundamental relational, regulatory, and cognitive vulnerabilities.

Theoretical Framework

The architecture of the Bergen Social Media Addiction Scale is anchored in two prominent psychological paradigms: the biopsychosocial components model of addiction formulated by Mark D. Griffiths (2005) and the developmental framework of attachment theory originated by John Bowlby (1969, 1973, 1980).

Griffiths’ Biopsychosocial Components Model

Griffiths’ (2005) theoretical framework posits that all addictions—whether chemical (e.g., substance use disorders) or behavioral (e.g., pathological gambling, compulsive video gaming, problematic internet use)—share common etiological mechanisms, neurobiological pathways, and phenomenological characteristics. Griffiths operationalized a universal taxonomy comprising six invariant criteria: salience, mood modification, tolerance, withdrawal, conflict, and relapse. According to this framework, an individual cannot be diagnosed with an authentic behavioral addiction solely based on high frequency of engagement or passionate affinity. Instead, addiction exists only when all six components converge simultaneously to create genuine clinical distress and psychosocial impairment.

By mapping each item of the BSMAS directly to one of these six universal components, the scale achieves direct parity with the nosological underpinnings of established addictive disorders, such as Gambling Disorder and Internet Gaming Disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). This operational consistency shields the instrument from contemporary criticisms surrounding the overpathologization of everyday digital life, providing a strict boundary between normal, high-frequency media consumption and true addictive symptomatology.

Attachment Theory as an Etiological Lens

The conceptual framework utilized by Monacis and colleagues (2017) incorporates Bowlby’s attachment theory to explicate the underlying psychosocial drives fueling compulsive digital connectivity. Bowlby asserted that early interactions with primary caregivers crystallize into enduring cognitive representations—internal working models—that govern how individuals navigate intimacy, stress, and relational security throughout the lifespan. Bartholomew and Horowitz (1991) expanded this into a four-category model defined by the orthogonal axes of attachment anxiety (the model of the self) and attachment avoidance (the model of others): secure, preoccupied (anxious), dismissing-avoidant, and fearful-avoidant.

In the digital realm, social media platforms function as continuous, low-risk relational ecosystems. Anxiously attached individuals exhibit a perpetual hypersensitivity to social rejection and an urgent need for belongingness; they turn compulsively to social media to monitor romantic partners, count social validations (e.g., “likes,” comments, shares), and stave off fears of exclusion. Avoidantly attached individuals, conversely, view intimate face-to-face interpersonal interactions as threatening and emotionally overwhelming; they use social media as an emotional buffer to achieve superficial social connection while maintaining safe, controlled interpersonal boundaries. By framing BSMAS scores within attachment theory, the scale serves as an empirical lens revealing how underlying relational insecurities and maladaptive coping styles translate into severe behavioral dependencies.

Validity

The psychometric validity of the Bergen Social Media Addiction Scale has been corroborated across numerous empirical evaluations, with rigorous documentation provided in the Italian validation study by Monacis, de Palo, Griffiths, and Sinatra (2017).

Construct and Factorial Validity

To examine the structural integrity of the construct, the validation team conducted Confirmatory Factor Analysis (CFA) on a large community and student sample (N = 734). Given that clinical screening data frequently display positive skewness and non-normal distributional curves, the researchers implemented the robust mean- and variance-adjusted maximum likelihood (MLMV) estimation procedure. The hypothesized unidimensional factor model demonstrated superior goodness-of-fit across global fit indices. The Comparative Fit Index reached 0.970, markedly exceeding the conventional ≥0.95 benchmark for superior model adequacy (Hu & Bentler, 1999). The Standardized Root Mean Square Residual was 0.030, well below the conservative ≤0.05 cutoff, confirming negligible residual covariance. Although the Root Mean Square Error of Approximation yielded a value of 0.094 (90% CI [0.069, 0.122]), this slight elevation is a well-documented mathematical artifact observed in models with low degrees of freedom (df = 9) and few indicators, rather than evidence of structural misspecification (Chen, 2007).

Measurement Invariance Across Demographics

A critical psychometric requirement for any screening tool is measurement invariance, which guarantees that observed test scores reflect identical latent constructs across disparate demographic cohorts without being distorted by measurement bias. Monacis and colleagues conducted exhaustive multigroup CFA across both gender (415 males vs. 319 females) and developmental age stages (270 adolescents aged 16–19 vs. 464 young adults aged 20–40). The testing evaluated a sequential hierarchy of increasingly restrictive nested models:

• Configural Invariance: Evaluated whether the baseline single-factor structure was identical across groups without parameter constraints. The configural model showed excellent fit across both gender (χ²/df = 3.65, CFI = 0.974, RMSEA = 0.085) and age cohorts (χ²/df = 3.86, CFI = 0.972, RMSEA = 0.088).
• Metric (Weak) Invariance: Constrained all factor loadings to equality across groups. Differences in model fit (ΔCFI ≤ 0.010 and ΔRMSEA ≤ 0.015, adhering to Cheung & Rensvold, 2002) confirmed that the individual scale items load onto the latent addiction construct with equal magnitude across males, females, adolescents, and adults.
• Scalar (Strong) Invariance: Constrained both factor loadings and item intercepts to equality across subgroups. The model fit remained stable, satisfying invariance criteria and demonstrating that mean differences in observed scores reflect true latent trait differences rather than cultural or demographic interpretation biases.
• Strict Invariance: Constrained item residual variances to equality across groups. Model fit remained robust (ΔCFI < 0.005), confirming that item measurement error is uniform across all evaluated demographics.

Convergent and Discriminant Validity

Convergent validity was substantiated through the Average Variance Extracted (AVE). The latent factor accounted for well over 50% of the variance among its indicators (AVE > 0.50), demonstrating that the items converge strongly on a single shared latent dimension. Criterion and convergent validity were further demonstrated through significant bivariate correlations with established behavioral addiction measures, including the Italian validation of the Internet Gaming Disorder Scale–Short-Form (IGDS9-SF; Monacis et al., 2016), and theoretical correlates such as insecure attachment styles assessed via the Attachment Style Questionnaire (Fossati et al., 2003).

Reliability

The reliability of the Bergen Social Media Addiction Scale has been substantiated through a rigorous suite of classical and modern psychometric indicators that extend beyond traditional internal consistency estimations.

Rather than relying solely on Cronbach’s alpha—which is known to be biased by item count and prone to misestimating reliability when tau-equivalence assumptions are violated—the validation researchers evaluated composite reliability, the Average Variance Extracted (AVE), the Standard Error of Measurement (SEM), and the Factor Determinacy coefficient:

• Internal Consistency & Composite Reliability: The six items demonstrate robust internal consistency, with standardized factor loadings all loading significantly and substantially on the single latent dimension (loadings ranging between 0.65 and 0.85). In independent replication studies across diverse international populations, the internal consistency of the BSMAS consistently yields Cronbach’s α and McDonald’s ω values between 0.86 and 0.92, indicating high internal homogeneity without redundant item phrasing.
• Average Variance Extracted (AVE): The AVE exceeded the conventional 0.50 threshold established by Fornell and Larcker (1981), demonstrating that the latent construct explains greater variance in its observable indicators than is attributable to idiosyncratic measurement error.
• Standard Error of Measurement (SEM): The calculated SEM across the observed scale range confirms narrow confidence intervals surrounding individual test scores. This precision ensures that clinicians can reliably detect genuine behavioral change or distinguish subclinical users from clinically significant cases without interference from measurement fluctuation.
• Factor Determinacy Coefficient: The factor determinacy index evaluated during the structural equation modeling analysis exceeded 0.90. This exceptionally high coefficient confirms that the factor score estimates correlate near-perfectly with the true latent trait, demonstrating that simple summative or averaged unit-weighted scale scores serve as reliable, statistically valid proxies for the latent behavioral addiction variable in multivariate and diagnostic models.

Factor Analysis

The underlying dimensionality of the BSMAS was evaluated using advanced Confirmatory Factor Analysis (CFA) performed in Mplus version 7.2 (Muthén & Muthén, 1998–2012). To account for the categorical-ordinal nature of 5-point Likert ratings and the positive skewness typical of clinical screening distributions, parameter estimation was conducted using the mean- and variance-adjusted maximum likelihood (MLMV) estimator, which provides robust standard errors and corrected test statistics.

A strictly unidimensional specification was tested, in which all six observable indicators freely loaded onto a single latent social media addiction factor, with the factor variance fixed to 1.0 for model identification and scale setting. The empirical results demonstrated robust structural stability:

• Chi-Square Goodness of Fit: Satorra-Bentler scaled χ² = 58.21, df = 9, p < 0.001. Although the test of exact fit was statistically significant due to the large sample size (N = 734), the relative chi-square ratio (χ²/df = 6.47) fell within acceptable bounds for exploratory behavioral indices.
• Comparative Fit Index (CFI): 0.970. This reflects exceptional incremental fit relative to the null independence model, comfortably exceeding the rigorous Hu and Bentler (1999) threshold of 0.950.
• Standardized Root Mean Square Residual (SRMR): 0.030. This confirms that the average discrepancy between observed and model-implied correlation matrices is exceptionally small, well below the conservative 0.050 benchmark.
• Root Mean Square Error of Approximation (RMSEA): 0.094 (90% CI [0.069, 0.122]). Although slightly above the traditional 0.08 cutoff, simulation studies have proved that RMSEA routinely over-rejects correctly specified single-factor models when the degrees of freedom are fewer than 10. The combination of CFI ≥ 0.97 and SRMR ≤ 0.03 confirms that the single-factor structure is an accurate structural representation of the empirical data.

All standardized factor loadings (λ) were positive, statistically significant at p < 0.001, and uniform in magnitude, ranging from 0.65 to 0.85 across the six items. This structural uniformity indicates that salience, mood modification, tolerance, withdrawal, conflict, and relapse contribute equitably to the operationalization of the latent addiction syndrome.

Instrument / Measurement Tool

The Bergen Social Media Addiction Scale is an ultra-brief self-report instrument designed for rapid administration in clinical, academic, and epidemiological settings.

• Test Type: Standardized self-report rating scale / psychological screening tool.
• Format: 6 core items corresponding to Griffiths’ (2005) six addiction components.
• Target Population: Adolescents, college students, and adults (validated for ages 16–40).
• Administration Modality: Self-administered (paper-and-pencil or digital survey format).
• Estimated Completion Time: Approximately 1 to 3 minutes when administered alone; up to 15 minutes when embedded within a comprehensive psychological battery.
• Response Scale: 5-point Likert-type scale with the following anchors:
  • 1 = Very rarely
  • 2 = Rarely
  • 3 = Sometimes
  • 4 = Often
  • 5 = Very often
• Scoring Instructions:
  • Composite Summative Score: Calculated by summing the raw scores of all 6 items. Total composite scores range from 6 to 30, with higher scores reflecting greater severity of problematic social media use.
  • Component Diagnostic Cutoff (Monothetic Approach): Under strict clinical criteria derived from DSM addiction models, an individual must score 4 (“Often”) or 5 (“Very often”) on at least five (or all six) of the items to be considered at high risk of clinical behavioral addiction.
  • Polythetic Screening Cutoff: In general mental health and epidemiological screening, a composite score threshold of 19 or higher (or scoring 3+ across multiple items) is frequently utilized to identify individuals displaying subclinical or problematic engagement warranting comprehensive assessment.

Permissions & Fee and Test Year

The Italian adaptation and psychometric validation of the Bergen Social Media Addiction Scale was published in 2017 by Lucia Monacis, Valeria de Palo, Mark D. Griffiths, and Maria Sinatra in the Journal of Behavioral Addictions. The parent instrument, the Bergen Facebook Addiction Scale, was originally developed and validated in 2012 by Cecilie Schou Andreassen and colleagues.

The BSMAS is an open-access psychometric instrument available free of charge for non-commercial academic research, pedagogical purposes, and non-profit clinical screening. Researchers and mental health practitioners are granted permission to administer, translate, and integrate the scale into survey batteries, provided that full scholarly attribution is given to the original scale authors and validation teams through proper citation of the seminal validation articles. Commercial applications, monetization within digital applications, or proprietary clinical diagnostic batteries may require formal authorization from the authors or copyright-holding academic publishers. Academic inquiries, collaboration requests, or translation permissions can be directed to the corresponding author, Dr. Lucia Monacis, at the University of Foggia ([email protected]).

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13. Items of the Scale (Questionnaire)