Social Media Bashing Assessment Scale (SM-BASH): Development and Psychometric Testing

Abstract

The pervasive use of social media exposes individuals to negative experiences, including social media bashing, which profoundly impacts mental health, yet there is a conspicuous lack of a standardized scale to assess these experiences. This study assessed the psychometric properties of the Social Media Bashing Assessment Scale (SM-BASH). A total of 978 college students from the Philippines participated in the study. An exploratory factor analysis (EFA) was conducted to determine the underlying structure of the SM-BASH, followed by a confirmatory factor analysis (CFA) to validate the model. The scale’s reliability was examined using Cronbach’s alpha and inter-item/inter-total correlation, while its validity was examined through content and criterion validity tests. The EFA and CFA revealed a two-factor structure model, which explained 70.57% of the total variance, comprising ‘Explicit Social Media Bashing’ and ‘Implicit Social Media Bashing’. The scale demonstrated excellent criterion validity and high internal consistency, with a Cronbach’s alpha of .92. The 10-item SM-BASH is a valid and reliable tool for quantifying negative encounters on social media platforms, particularly among student populations. Its strong psychometric properties make it appropriate for use in research and potentially in clinical settings to assess and address the impacts of social media bashing on individuals’ well-being.

Keywords

Social media psychometric properties online negativity mental health college students bashing

Introduction

Globally, there are approximately 5.44 billion internet users, representing 67.1% of the world’s population, with around 5.07 billion individuals, or 62.6%, engaging in social media activities (Statista, 2024). Projections suggest that by 2027, the number of social media users will surpass 5.85 billion. Among the different social media platforms, YouTube has the greatest number of users, followed by WhatsApp, Facebook, Instagram, Facebook Messenger, and TikTok (Pew Research Center, 2024). With billions of users worldwide engaging in online communities and social media, the prevalence of negative interactions has also risen (Valkenburg, van Driel, & Beyens, 2022). In recent years, this widespread adoption of social media platforms has created a vast digital landscape where individuals are constantly exposed to diverse perspectives, opinions, and behaviors, including both positive and negative content (Luttrell & Wallace, 2021; Valkenburg, van Driel, & Beyens, 2022). Alongside these advantages, social media use also exposes individuals to various negative experiences, such as cyberbullying, harassment, cyberstalking, and social exclusion (Çetin et al., 2011; Lam & Li, 2013; Valenzuela-García et al., 2023), resulting in various mental health issues (Steinsbekk et al., 2023).

Background Literature

“Bashing” refers to the use of harsh and abusive verbal insults, which, when combined with social media, encompasses publicly criticizing, attacking, or harassing someone via social media platforms (Bongon et al., 2020). This behavior is prevalent on online public platforms and may involve making derogatory comments, spreading false information, ridiculing, or targeting individuals or groups with negative and aggressive interactions (Ouvrein et al., 2020; Valkenburg, Beyens, et al., 2022). Although these phenomena involve negative online interactions, social media bashing specifically pertains to public criticism and harassment on social platforms, which can be more pervasive and damaging due to the public and often viral nature of these interactions (Abarna et al., 2022; Valenzuela-García et al., 2023).

Compared to the literature on cyberbullying, harassment, and online negativity (Stevens et al., 2021; Thomas et al., 2021), the literature on social media bashing is limited, primarily focusing on the online targeting of celebrities and political figures (Ouvrein et al., 2020). Cyberbullying is defined as deliberate, repeated, and hostile behavior using digital platforms to harm individuals, often involving a power imbalance and resulting in significant psychological distress (Çetin et al., 2011; Stewart et al., 2014). Social media bashing, in contrast, refers to widespread criticism or ridicule, often driven by public outrage or polarizing events, and typically lacks the sustained targeting or premeditation characteristic of cyberbullying (Bongon et al., 2020; Ouvrein et al., 2024). While cyberbullying involves personal and repetitive attacks (Menin et al., 2021; Topcu & Erdur-Baker, 2010), social media bashing is often episodic, collective, and may emerge spontaneously (Ouvrein et al., 2024). The large-scale participation in social media bashing amplifies its impact, leading to reputational harm, emotional distress, and potential real-world consequences. Unlike cyberbullying, which often occurs within existing relationships (Giumetti & Kowalski, 2022), social media bashing frequently involves strangers, fostering anonymity and moral disengagement (Bongon et al., 2020; Ouvrein et al., 2024).

Evidence has shown that exposure to negative content and interactions on social media can exacerbate feelings of anxiety, and depression, and negatively affect overall mental health (Bettmann et al., 2021). Increased use of social media has consistently been linked to poor sleep quality, psychological distress, decreased quality of life, and increased suicide risk (Shen et al., 2020; Wong et al., 2020). Constant exposure to negative content may erode individuals’ trust in online interactions, resulting in loneliness and isolation (Thomas et al., 2020). The consequence of negative social media interactions may also spill over into offline life, affecting academic performance, relationships, and overall life satisfaction (Kholhar et al., 2021; Raza et al., 2020). The emotional burden of dealing with online negativity and social media overload can reduce students’ ability to concentrate on their studies, leading to lower academic achievement and decreased motivation (Wehlan et al., 2020). Socially, it can create conflicts and tensions in face-to-face interactions, exacerbating feelings of isolation and loneliness (Thomas et al., 2020).

Social media bashing is not exclusive to celebrities, influencers, or politicians; young adults are also vulnerable to its effects due to their extensive engagement with social media platforms. In the United States alone, young adults constitute a significant portion of social media users, with approximately 56.4 million active users (Statista, 2024). This demographic, including college students, spends a substantial amount of time on social media, making them particularly susceptible to negative online experiences (Keum et al., 2023). During this period, individuals are especially sensitive to social feedback and validation, both online and offline (Flett et al., 2014). Consequently, negative interactions on social media platforms can profoundly impact their psychological well-being and socioemotional development (Smith et al., 2021). Understanding the heightened vulnerability of young adults to social media bashing underscores the importance of addressing and mitigating its effects within this demographic.

Despite the growing body of evidence on the adverse effects of social media negativity, there is a notable gap in the availability of standardized scales specifically designed to measure the extent of social media bashing. Existing measures often focus broadly on cyberbullying or general internet use, without adequately capturing the nuanced and varied forms of negativity encountered on social media platforms. For instance, while tools like the Cyberbullying Victimization Scale (Stewart et al., 2014), the Cyber Victim and Bullying Scale (Çetin et al., 2011), the E-Victimization Scale, and the E-Bullying Scale (Lam & Li, 2013), and the Cyber Bullying Inventory (Topcu & Erdur-Baker, 2010) provide valuable insights, these scales do not fully address the specific contexts and mechanisms through which social media bashing occurs. This limitation hampers the ability of researchers and clinicians to accurately assess and address the impacts of social media bashing on mental health and overall well-being. As social media platforms continue to develop new features and ways for users to interact, the forms and intensity of negative experiences may also change. This ongoing evolution necessitates a measurement tool that can adapt and remain relevant in capturing the current realities of social media use. Hence, this study was conducted to develop a comprehensive and psychometrically sound tool specifically designed to measure the extent of negative experiences on social media platforms.

Theoretical Framework

While no specific theory exclusively addresses social media bashing, the Social Comparison Theory (Festinger, 1957) and the Online Disinhibition Effect (Suler, 2004) provided valuable theoretical guidance for conceptualizing this phenomenon. The Social Comparison Theory explains how individuals evaluate their self-worth, opinions, and abilities by comparing themselves to others (Festinger, 1957; Suls & Wills, 2024). On social media, these comparisons are frequent due to the constant exposure to curated content. Upward comparisons, leading to envy or feelings of inadequacy, and downward comparisons, fostering superiority or judgment, may both play roles in the motivations behind social media bashing (Festinger, 1957; Suls & Wills, 2024). This theory highlights how public criticism or ridicule on social media can stem from personal insecurities, envy, or the need for validation within an online community. The Online Disinhibition Effect, on the other hand, sheds light on how the online environment lowers inhibitions and fosters hostile behavior. Factors such as anonymity, invisibility, and the lack of immediate consequences encourage users to express themselves more freely, often in ways they would avoid in face-to-face interactions (Suler, 2004). This disinhibition creates a fertile ground for the emergence of large-scale criticism or bashing, particularly in response to polarizing events or public outrage (Suler, 2005). Together, these theories provide a foundation for understanding the psychological and environmental factors that contribute to social media bashing. By framing the SM-BASH within these theoretical perspectives, the scale aims to capture the complex interplay of individual motivations, social dynamics, and platform-driven influences underlying this behavior.

Research Aim

The aim of this study was to develop and validate the Social Media Bashing Assessment Scale (SM-BASH), a tool designed to measure the extent of negative experiences, including being criticized, attacked, or harassed, on social media platforms among college students.

Methods

Design

This study employed an exploratory sequential research design, which involves two main phases: a qualitative phase to develop the scale and a quantitative phase to validate it. The exploratory sequential research design is particularly useful in situations where the constructs of interest are complex or not well-defined, and there is limited existing literature or theoretical guidance (Cameron, 2009). It allows researchers to iteratively refine their understanding of the constructs and develop a scale that accurately captures the intended phenomena.

Scale Development

Item Generation

The framework proposed by Boateng et al. (2018) served as the guiding framework for the process of scale development and validation. The initial pool of scale items was derived from a comprehensive review of existing literature on social media negativity, cyberbullying, and online harassment, resulting in 11 items. Additionally, individual interviews were conducted with 10 students to gain a deeper understanding of their experiences and exposure to negative online interactions. The data collected from these interviews underwent content analysis, leading to the generation of an additional 12 items. These newly generated items were then compared to those derived from the literature review, resulting in the deletion of 5 items. The deletion of 5 items was based on the results of a comparison between the items generated from the literature review and those derived from the individual interviews. The justification for this deletion was twofold. First, the content analysis of the interview data highlighted that certain items from the literature review did not align closely with the specific experiences or concerns expressed by the participants. These items were deemed less relevant to the context of social media bashing as experienced by the target population. Second, some items were found to overlap in content, leading to redundancy.

Content Validity and Face Validity

The items were validated by five experts, including two psychologists specializing in behavioral research, two social media researchers with extensive experience in analyzing online behavior and digital interactions, and a mental health professional with a focus on the psychological impact of online activities. Their diverse expertise ensured the relevance and representativeness of the construct being measured, providing a robust foundation for the content validity of the scale. Their feedback and suggestions were carefully considered to ensure that the items adequately captured the experiences of social media bashing. Using the Content Validity Index (CVI) both at the item level (I-CVI) and the scale level (S-CVI/average), the content validity was measured. For the I-CVI calculation, each item was rated by the experts on a four-point scale, ranging from one (not relevant) to four (highly relevant), based on its relevance and clarity. Items with an I-CVI below 0.70 and an S-CVI/average below 0.90 were considered for potential removal from the scale (Shi et al., 2012).

Following the refinement process, the drafted scale was distributed to a sample of 20 college students to assess its face validity. Students were asked to review the items and provide feedback on their clarity, comprehensibility, and relevance to their experiences with social media. Their responses were analyzed to gauge the scale’s overall acceptability and appropriateness for measuring social media bashing.

Reliability

Inter-item/inter-total correlation and Cronbach’s α were utilized to examine the internal consistency of the developed scale. Cronbach’s α scores greater than 0.70 indicated sufficient reliability, while inter-item total correlation ranging from 0.30 to 0.70 was considered desirable (Heale & Twycross, 2015).

Survey Administration and Sample Size

A sample of 978 college students enrolled in three public universities in the Philippines was included in this study. The sample size was based on the number of items developed. As a rule of thumb, there should be at least 10 participants for each item of the scale, ideally at a ratio of 15:1 or 20:1 (DeVellis, 2006). Participants included college students across year levels and gender backgrounds who were currently registered in the health science programs of the universities.

Measures

To assess students’ perceptions of life satisfaction, the Life Satisfaction Scale was administered, comprising five items rated on a seven-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). Previous studies involving university students demonstrated strong psychometric properties, with an internal consistency value of 0.91 (Pavot & Diener, 2008).

For assessing students’ perceptions of their mental health, a single-item scale developed by Casu and Gremigni (2019) was utilized. Participants rated their overall mental health during the past few weeks on a 5-point Likert-type scale (1 = poor to 5 = excellent), with higher scores indicative of better mental health. This measure’s reliability and convergent validity were established in prior research (Casu & Gremigni, 2019).

To examine students’ perceptions of stress, the 4-item Perceived Stress Scale (PSS) was employed (Cohen et al., 1994). Participants responded to each item using a Likert scale ranging from 0 (never) to 4 (very often), yielding a score range of 0–16, with higher scores reflecting elevated stress levels. Earlier studies have confirmed the scale’s predictive validity and reliability, with a Cronbach’s alpha of 0.83 (Cohen et al., 1994).

Ethical Considerations and Data Collection

The Institutional Review Board of [redacted for review] granted ethical clearance for this study. Before commencing the field survey, all identified participants provided informed written consent, ensuring their voluntary participation. Detailed information about the research objectives, procedures, potential risks, and benefits was provided, and participants were given the opportunity to seek clarifications and ask questions before giving consent. During the qualitative phase, individual interviews were conducted with selected students to explore their experiences regarding online negative interactions. With participants’ consent, the interviews were audio-recorded and transcribed verbatim for analysis. In the quantitative phase, the scale was administered via questionnaires to a larger sample of students. Participants were assured that their responses would be treated confidentially, with no identifying information linked to their answers. To maintain anonymity, each participant was assigned a unique identification code, and data were securely stored with limited access for research purposes only.

Data Analysis

The Kaiser-Meyer-Olkin (KMO) measure was employed to evaluate whether the items exhibited a compact pattern of inter-correlations, with values of 0.5 or higher considered adequate (Kaiser, 1974). Additionally, the Bartlett Test was utilized to determine if the variables were sufficiently correlated to justify factor analysis, with a significant result indicating that the items were not entirely uncorrelated. To determine the appropriate number of factors to extract, Initial Eigenvalues were examined, with values greater than 1 considered suitable for retaining a factor. The principal component method of extraction and varimax rotation were utilized, with all factor loadings below .45 suppressed, and items with cross-loadings excluded. Internal consistency was assessed using Cronbach’s alpha, with values of .70 and above considered acceptable (Schmitt, 1996). Confirmatory Factor Analysis (CFA) was conducted using AMOS Graphic version 23, with parameters and cut-offs based on Gaskin and Lim (2016). These include a Root Mean Square Error of Approximation (RMSEA) ranging from <.050 to .080 (though RMSEA between .06 and .08 is deemed poor, as per Hu & Bentler, 1998). Additionally, a Comparative Fit Index (CFI) range of .95–.99, CMIN/DF > .20, SRMR <0.08, and PClose >0.05 were considered.

Results

Student Characteristics

The characteristics of the sample population (n = 978) indicate an average age of 19.78 years (SD = 1.80), with the majority being female (80.8%). Regarding year level, the highest representation is from first students (44.2%), followed by third students (27.7%). Most participants reside with their families (69.1%) rather than on-campus (8.1%) or off-campus (22.8%). In terms of mental health, the majority report fair mental health (44.2%), while internet use is evenly distributed between 3 to 6 hours (52.6%) and over 6 hours (32.3%). Self-reported technological competence varies, with the largest proportion rating their competence as average (44.4%), followed by above average (32.9%). The sample population was divided into two groups: Group A consisted of 490 participants, while Group B comprised 488 participants.

Content Validity

The Item-Content Validity Index (I-CVI) of the scale ranged from 0.70 to 1, which was within the recommended value range (>0.70 points) (Polit & Beck, 2018). Three items were excluded, leading to a refinement of the scale to 15 items. The overall Scale-Content Validity Index (S-CVI) reached 0.90 points, indicating strong content validity. Following an item-total correlation analysis, five additional items were removed, as their corrected item-totals fell below .0.30. Consequently, the draft scale was finalized at 10 items (Table 1).

Table 1.

Content Validity Index (CVI) and Item-Total Correlations for the SM-BASH Scale.

Items	Expert ratings (Relevant: 3 or 4)	I-CVI	Interpretation	Item total correlations
Item 1	5/5	1.00	Highly relevant	0.35
Item 2	4/5	0.80	Highly relevant	0.42
Item 3	5/5	1.00	Highly relevant	0.48
Item 4	4/5	0.80	Highly relevant	0.39
Item 5	4/5	0.80	Highly relevant	0.50
Item 6	5/5	1.00	Highly relevant	0.45
Item 7	3/5	0.70	Moderately relevant	0.37
Item 8	4/5	0.80	Highly relevant	0.41
Item 9	5/5	1.00	Highly relevant	0.55
Item 10	4/5	0.80	Highly relevant	0.60

Factor Analysis

Exploratory factor analysis showed that the Kaiser-Meyer-Olkin (KMO) was adequate (KMO = .912), and the Bartlett test of Sphericity was significant at p < .001 (χ2 = 3309.328). Principal component analysis on the 10 items using the varimax rotation method revealed 2 factors with eigenvalues ≥1.0. The scree plot clearly showed that two factors were extracted (Figure 1). These two factors accounted for 70.57% of the total variance. The two factors were labeled as ‘explicit social media bashing’ and ‘implicit social media bashing’ (Table 2).

Figure 1.

Scree plot of eigenvalues for the SM-BASH scale.

Table 2.

Factor Loadings, Eigenvalues, and Explained Variance for the SM-BASH Scale.

SMBAS items	Component
SMBAS items	1	2
Item 1	.771	-
Item 2	.870	-
Item 3	.848	-
Item 4	.781	-
Item 5	.771	-
Item 6	.686	-
Item 7	-	.869
Item 8	-	.886
Item 9	-	.654
Item 10	-	.629
Eigenvalues	5.815	1.242
Explained variance (%)	58.148	12.425
Cumulative variance (%)	58.148	70.572

The two-factor model identified in the EFA was further verified through the CFA (Figure 2). Although the value of RMSEA was initially poor at .16, it became acceptable at .06 when the suggested error covariances were added from items 8 to 7, 2 to 1, 5 to 1, and 2 to 3. The value of PClose was .05, supporting that the RMSEA value was excellent. The factor correlation between factor 1 and 2 was r = .84 (n = 488, p < .001). The other fit parameters from the CFA, such as the comparative fit index (CFI) was .98, which was an excellent fit. Similarly, the CMIN/DF, which compares if the observed variables and expected results are acceptable, was adequate at 3.12. The Standardized Root Mean Square Residual (SRMR) was excellent at .03.

Figure 2.

Confirmatory factor analysis (CFA) model showing the factor structure of the SMBAS scale.

Reliability

Factor 1, the ‘explicit social media bashing’, demonstrated high reliability with a Cronbach’s alpha of .91. Factor 2, ‘implicit social media bashing’, had a Cronbach’s alpha of .85, indicating good internal consistency. Overall, the total scale showed high reliability with a Cronbach’s alpha of .92 (Table 3).

Table 3.

Internal Consistency Reliability of the SMBAS Scale.

Factors	Items	Mean	SD	Cronbach alpha
1. Explicit social media bashing	6	1.622	.836	.91
2. Implicit social media bashing	4	1.939	.949	.85
SM-BASH total scale	10	1.794	.812	.92

Criterion Validity

Correlational analyses showed significant associations between SM-BASH scores and social media bashing, mental health, life satisfaction, and stress. Higher levels of social media bashing were associated with poorer mental health (r = −.147, p < .001) and life satisfaction (r = −.108, p < .001), and increased stress levels (r = .161, p < .001) (Table 4).

Table 4.

Correlation Matrix for Social Media Bashing, Mental Health, Life Satisfaction, and Stress.

Scale	Mean	SD	1	2	3	4
1. Social media bashing	1.749	.812	1
2. Mental health	2.867	.946	−.147**	2
3. Life satisfaction	3.051	.816	−.108**	.394**	3
4. Stress	3.505	.808	.161**	−313**	−.184**	4

Discussion

As social media usage continues to rise, so too does the potential for negative experiences, such as cyberbullying, harassment, and social exclusion, which can have significant detrimental effects on individuals’ mental health and overall well-being (Çetin et al., 2011; Lam & Li, 2013; Valenzuela-García et al., 2023). Without a standardized scale to assess these experiences, researchers, practitioners, and policymakers lack a comprehensive tool to accurately quantify and address the prevalence and impact of social media bashing. Therefore, the newly developed scale could fill a crucial gap in the field by providing a reliable and valid instrument to systematically evaluate students’ encounters with negative interactions on social media platforms, ultimately contributing to the development of evidence-based measures and policies to promote safer and healthier online environments.

Overall, the findings of this study underscore the robust psychometric properties of the SM-BASH. The high internal consistency, as indicated by a Cronbach’s alpha of .92, reflects the excellent reliability of the scale in assessing the extent of negative experiences on social media platforms. This aligns with previous research emphasizing the importance of internal consistency in ensuring the reliability of measurement instruments (Heale & Twycross, 2015). Moreover, the correlations observed between SM-BASH scores and other measures, including mental health, life satisfaction, and stress, indicate excellent criterion validity. These findings indicate that when students experience higher levels of social media bashing, they tend to experience poorer mental health, lower life satisfaction, and increased stress levels. This result corroborates earlier literature linking negative social media experiences to various adverse psychological outcomes (Shen et al., 2020; Steinsbekk et al., 2023; Wong et al., 2020). Finally, the confirmation of this two-factor model through CFA further strengthens the validity of the SM-BASH. These factors capture distinct dimensions of social media bashing, allowing for a nuanced understanding of the various ways in which individuals experience bashing on social media platforms.

Factor analyses revealed two distinct factors: explicit social media bashing and implicit social media bashing (Table S1). The former involves direct acts of aggression such as cyberbullying and targeted attacks, while the latter encompasses feelings of exclusion and marginalization within online social networks. Understanding and measuring these subscales are crucial for comprehensively addressing the multifaceted nature of social media bashing and its detrimental impacts on individuals’ psychological well-being and overall quality of life.

‘Explicit Social Media Bashing’, as one of the identified factors, encompasses overt acts of aggression or intimidation directed towards an individual on social media platforms. This includes behaviors such as cyberbullying, online harassment, and targeted attacks, which have been extensively documented in previous research (Stewart et al., 2014; Topcu & Erdur-Baker, 2010). The inclusion of direct harassment as a factor in the SM-BASH underscores the significance of addressing explicit forms of negativity in online environments, as these behaviors can have severe consequences for individuals’ psychological well-being and overall quality of life (Bettmann et al., 2021; Valkenburg, van Driel, & Beyens, 2022).

‘Implicit Social Media Bashing’ represents a more subtle yet equally impactful aspect of social media bashing. This factor encapsulates instances where individuals feel excluded or marginalized within their online social networks, experiencing feelings of isolation and alienation. Research suggests that social network exclusion can be just as harmful as direct harassment, contributing to increased levels of loneliness, decreased self-esteem, and compromised mental health (Chiou et al., 2015; Covert & Stefanone, 2020). By including social network exclusion as a factor in the SM-BASH, the scale acknowledges the complex social dynamics at play in online interactions and underscores the importance of addressing subtle forms of negativity that may go unnoticed but have profound effects on individuals' well-being.

Implications for Practice

The utilization of a valid and reliable scale, such as the SM-BASH, for measuring the extent of negative experiences on social media platforms among students, could guide research, practice, and policy efforts toward creating a more inclusive, supportive, and respectful online environment for all users. By recognizing both overt (explicit social media bashing) and subtle forms (implicit social media bashing) of negativity, the scale could offer a comprehensive framework for assessing individuals’ encounters on social media platforms. This knowledge could inform the formulation of empirically based measures and policies aimed at promoting safer and healthier online interactions. Educational institutions and social media platforms can leverage the scale to develop proactive strategies for fostering positive online communities and mitigating the impact of social media bashing on individuals’ mental health and well-being. Researchers can utilize the scale to explore the prevalence, impact, and underlying mechanisms of social media bashing across various populations and contexts. Mental health professionals and educators can integrate the SM-BASH into screening and intervention protocols to identify individuals at risk and offer targeted support. Finally, policymakers and stakeholders can utilize the scale to inform evidence-based strategies and initiatives aimed at preventing cyberbullying, enhancing digital literacy, and fostering positive online interactions.

Limitations and Future Research

While the scale demonstrated excellent reliability and validity among college students in the Philippines, its applicability to other populations and cultural contexts remains uncertain due to the limited diversity in the sample. Including only college students may introduce selection bias, limiting the scale’s ability to represent different age groups, professions, or educational backgrounds, and reducing its generalizability to broader populations. Additionally, the cultural context of Filipino college students may have influenced the results, as cultural norms and values can shape perceptions and experiences of social media bashing. Another limitation is the gender imbalance in the sample, with 80.8% female representation, which may have influenced the findings and calls for greater inclusion of male participants in future studies to enhance representativeness. The SM-BASH also relies on self-reported responses, which may be affected by factors such as recall bias, potentially impacting the accuracy of the data collected. Moreover, the scale’s development and validation were conducted using a cross-sectional design, restricting its ability to assess changes in social media bashing experiences over time or establish causal relationships. Future research should prioritize a more diverse and gender-balanced sample to improve generalizability, as well as explore the cultural nuances influencing social media bashing. Employing longitudinal or mixed-methods designs could also address these limitations and provide a more comprehensive understanding of social media bashing and its effects on individuals’ well-being.

Conclusion

The 10-item SM-BASH emerged as a robust instrument for quantifying the prevalence of harsh and abusive verbal insults experienced by individuals on social media platforms, particularly among students. Through rigorous psychometric testing, the SM-BASH demonstrated high reliability and validity in capturing individuals’ encounters with social media bashing. By delineating two distinct factors – explicit social media bashing and implicit social media bashing – the scale offers a nuanced framework for comprehensively assessing the multifaceted nature of online negativity. This tool has the potential to increase our understanding of the detrimental effects of social media bashing and guide the development of interventions aimed at mitigating its impact on individuals’ well-being in the digital age.

Supplemental Material

Supplemental Material - Social Media Bashing Assessment Scale (SM-BASH): Development and Psychometric Testing

Supplemental Material for Social Media Bashing Assessment Scale (SM-BASH): Development and Psychometric Testing by Leodoro J. Labrague, and Chidozie E. Nwafor in Psychological Reports

Footnotes

Acknowledgements

The expertise of Dr Anthony Monnae is acknowledged.

Author Contributions

Leodoro J. Labrague: Conceptualization, Methodology, Software Data curation, Writing- Original draft preparation. Visualization, Investigation. Supervision.: Software, Validation.: Writing- Reviewing and Editing. Chidozie E. Nwafor: Conceptualization, Methodology, Writing- Original draft preparation. Visualization, Investigation. Writing- Reviewing and Editing.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

ORCID iD

Leodoro J. Labrague

Data Availability Statement

The data that support the findings of the study are available from the corresponding author upon reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Leodoro J. Labrague is an assistant professor at the School of Nursing and Healthcare Leadeshio at the University of Washington USA.

Chidozie E. Nwafor is a Developmental Psychologist at Nnamdi Azikiwe University.

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