social media addiction methodology

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• Published: 04 October 2023

Predictors of social networking service addiction

• Hyeon Jo   ORCID: orcid.org/0000-0001-7442-4736 1 &
• Eun-Mi Baek   ORCID: orcid.org/0000-0002-0940-5819 2  

Scientific Reports volume  13 , Article number:  16705 ( 2023 ) Cite this article

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The surge in social network services (SNS) usage has ignited concerns about potential addictive behaviors stemming from excessive engagement. This research focuses on pinpointing the primary determinants of SNS addiction by introducing a theoretical framework centered on flow, perceived enjoyment, and habit. A sample of 282 SNS users from South Korea was surveyed, and the gathered data was assessed through partial least squares structural equation modeling (PLS-SEM). The evaluation revealed that positive affect closely relates to flow and perceived enjoyment, whereas negative affect amplifies flow but diminishes perceived enjoyment. Additionally, the research underscored that social influence significantly shapes habits and affects perceived enjoyment. Notably, flow demonstrated a strong connection to addiction, and perceived enjoyment influenced both flow and habit significantly. Habit was directly linked to addiction. These insights pave the way for more in-depth studies on SNS addiction patterns and offer a foundation for devising effective strategies to mitigate its adverse effects.

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Introduction.

With the rapid proliferation of social network services (SNS), users' daily routines have evolved significantly 1 , 2 , 3 . SNS provides insights into acquaintances' updates, new product launches, and current events 4 , 5 , 6 . Given SNS's profound influence, users are dedicating more time to these platforms 7 . Among various tools, SNS apps are most frequented on smart devices 8 . Some users develop a habitual pattern of SNS usage, which, in extreme cases, turns addictive 9 , 10 . SNS addiction can be defined as an excessive, compulsive use of social media platforms that interferes with daily life, leading to negative consequences in physical, social, and mental well-being 11 . It involves an increased craving to engage on these platforms, leading to the neglect of offline relationships and daily responsibilities 12 . Ironically, this addiction can overshadow the genuine connections that SNSs aim to cultivate. Numerous studies have explored the variables driving SNS user behavior 13 , 14 , attributing psychological elements, social influencers, and predisposition to SNS addiction 15 , 16 , 17 . However, there remains a gap in comprehensively understanding social and psychological influences on addiction. This research seeks to bridge that gap by examining user affect, social stimuli, and mental states.

The rise in SNS over-reliance or addiction is a modern behavioral addiction that alarms researchers and mental health experts alike 11 . This trend is notably prevalent among university students, a major user group. Analyzing SNS addiction determinants among this group is crucial for multiple reasons. Firstly, high SNS engagement in students correlates with adverse psychological outcomes like depression, anxiety, and loneliness 18 , 19 . It also interferes with sleep and hampers academic success 20 . Thus, comprehending SNS addiction's roots can help alleviate these issues. Furthermore, university students are in a crucial life phase, establishing habits that might extend into their later life 21 . Recognizing and addressing addictive patterns during this period can circumvent future repercussions 22 . As a dominant SNS user group, understanding students' addiction can enable tailored intervention strategies. Hence, exploring SNS addiction's drivers among university students can advance mental health, foster efficient interventions, and deepen our grasp on behavioral addiction.

The dual factor model of Facebook use 23 posits that individuals use SNS to manage both positive and negative affects. Positive affect boosts user satisfaction and flow in SNS 24 . Users in a good mood can lose track of time on SNS, further enhancing their addictive tendencies. Negative affect, denoting distressing emotions 25 , also impacts SNS flow 24 . Those with high negative affect might use SNS reflexively to counter negative feelings. This behavior aligns with the manifestation of habits 26 . Higher negative affect can diminish enjoyment levels. Users might engage with these platforms to amplify positive feelings or mitigate negative ones. If such strategies become compulsive, they might foster SNS addiction. Moreover, individuals with pronounced negative affect might be susceptible to behavioral addictions like SNS addiction 27 , using them as coping mechanisms. While positive affect usually brings beneficial outcomes, in some contexts, it can contribute to SNS addiction. If SNS consistently evokes positive feelings, it might reinforce and lead to an addiction cycle. Some research highlights a positive correlation between positive affect and addictive behaviors 28 . Considering both affects allows a thorough study of the emotional aspects of SNS addiction among university students. These insights can guide the creation of interventions targeting addictive SNS behaviors.

Social influence represents the degree to which a person's attitudes, beliefs, and behaviors are affected by others 29 . Users who are highly influenced by acquaintances will use SNS more frequently. Users who use SNS more tend to become addicted to SNS 30 . Thus, users with a higher level of social influence may be more prone to SNS addiction. Users who hear a lot about SNS from their acquaintances may be more immersed in using SNS than those who do not. They may also habitually use SNS to check the influence of their surroundings. Because SNS essentially forms fun and motivation to use via social relationships, social influence may increase perceived enjoyment. Additionally, this study investigates the impacts of negative affect on addiction, flow, habit, and perceived enjoyment.

Flow, a concept introduced by Csikszentmihalyi 31 , describes the state where one becomes deeply engrossed in an activity, losing all sense of time and self-awareness. Such flow significantly influences online users' addiction 32 , 33 . Regarding SNS, flow is viewed as an addiction precursor 34 . This immersion makes users neglect other priorities, encouraging addictive behaviors. Perceived enjoyment is another driver. As per self-determination theory 35 , inherent satisfaction from activities motivates behavior. Thus, students enjoying SNS may overuse, leading to addiction 33 , 36 . Perceived enjoyment also impacts flow 37 , 38 . Habit, marked by automatic responses and lack of intent 39 , also drives SNS addiction, especially among university students. Habitual SNS usage can be an automatic reflex to triggers like boredom, potentially escalating to addiction 40 . Habit strength, denoted by SNS usage frequency, is a predictor of addiction 10 , 30 , 41 . By studying flow, enjoyment, and habit, we obtain a holistic view of the psychological dynamics underlying SNS addiction.

The primary objective of this study is to comprehensively examine the multifaceted relationship between individual emotional responses (both positive and negative affects), social influences, flow experiences, perceived enjoyment, habitual behaviors, and the potential development of addiction towards SNS among university students. Through this exploration, the research aims to shed light on the underlying psychological and behavioral dynamics that may predispose individuals to SNS addiction, thereby offering insights into potential intervention and prevention strategies tailored to this demographic.

Although there is an extensive body of literature addressing the determinants of addiction to SNS, certain theoretical gaps remain unbridged. Firstly, the bulk of the research tends to position positive and negative affect on a singular continuum, rather than recognizing them as distinct constructs. This oversimplification potentially obscures the individual contributions of each affective state to SNS addiction. Secondly, the intricate relationships between positive and negative affect and other psychological determinants, including flow, perceived enjoyment, and habit, remain underexplored, particularly within the demographic of university students. This paper addresses the above gaps in the literature and contributes to the field in following ways. Firstly, it takes a novel approach by considering positive affect and negative affect as independent variables, allowing for a more nuanced understanding of their respective roles in SNS addiction. Secondly, it extends the current literature by exploring the interplay between positive affect, negative affect, social influence, flow, perceived enjoyment, and habit in the context of SNS addiction among university students. The conceptual foundation of our study is rooted in the dual factor model of SNS Use, which emphasizes the regulatory function of both positive and negative affects in SNS engagement 23 . Anchored by Deci and Ryan’s self-determination theory 35 , we propose that inherent satisfaction derived from activities becomes a potent motivator of behavior, especially in the context of SNS usage. Csikszentmihalyi’s theory of Flow also informs our study, positing that users become deeply engrossed in activities, losing awareness of time, which, in the context of SNS, can precipitate addiction. Collectively, this theoretical framework will guide our exploration of the psychological and behavioral nuances influencing SNS addiction among university students.

This paper delves into such an unresearched dimension, shedding light on the intricate interplay of flow, habits, and perceived enjoyment as drivers of SNS addiction. While extant literature has ventured into the domains of health, loneliness, and attachment in relation to SNS addiction, the unique combination of factors examined herein offers a fresh perspective. This underscores the originality of our research, marking a distinct departure from conventional narratives. By merging previously disjointed variables and unveiling their collective impact on SNS addiction, we not only bridge a significant gap in the current scholarship but also provide readers with a compelling rationale to delve deeper into our findings. In so doing, we aspire to catalyze further academic discourse and innovative research in this domain.

This article is structured as follows. Section “ Literature review ” describes the theoretical background. Section “ Research model ” delineates the research model and hypotheses. Section “ Methodology ” covers data and measurement tools. Section “ Results ” presents the analysis results of the measurement model and structural model. Section “ Discussion ” shows the discussion. Finally, Section “ Conclusion ” introduces a summary, implications, and limitations.

Literature review

Over the past few decades, there has been a meteoric rise in the popularity and reach of SNS. This expansion has not only changed the way individuals communicate and interact but has also paved the way for a vast digital market. The ubiquitous nature of these platforms, combined with their design geared towards continuous engagement, has led to growing concerns among researchers, psychologists, and sociologists. The core of these concerns revolves around the potential addictive nature of these platforms. Given the profound impact of SNS on modern life, an increasing body of research has been dedicated to exploring the phenomenon of SNS addiction, its underlying causes, and its multifaceted implications on individual and societal well-being.

Affect, broadly categorized into positive and negative emotions, plays a pivotal role in determining how individuals interact with, perceive, and are influenced by SNS platforms. SNS addiction, characterized by excessive and compulsive use of SNS platforms despite negative repercussions 42 , has been closely tied to affective states. Positive affect often drives the 'reward-seeking' behavior, causing users to chase the dopamine rush associated with likes, comments, and social validation on these platforms 41 . Conversely, negative affect often results in escapism, where users resort to SNS to avoid or numb their negative feelings, eventually leading to addictive patterns 43 . Positive emotions have been linked to increased engagement with SNS platforms. Users in a positive mood state tend to share more, interact positively with others, and spend more time on SNS 44 , 45 , 46 . Furthermore, positive affect enhances the intrinsic motivation to use SNS, increasing the frequency and duration of usage 47 . There is evidence to suggest that people with high levels of positive affect use SNS as a medium to maintain and strengthen social connections, amplifying their feelings of social belonging and self-worth 48 . Contrarily, negative affect has a more nuanced relationship with SNS. While some research indicates that individuals experiencing negative emotions resort to SNS as a coping mechanism 49 , others argue that prolonged SNS use, especially passive browsing, can exacerbate negative emotions 50 . Moreover, the “social comparison theory” postulates that individuals with high levels of negative affect are more prone to compare themselves to others on SNS, which can amplify feelings of inadequacy and further deepen the negative emotional state 51 . In addition to affect, several studies have introduced attachments to explain SNS addiction. Monacis et al. 52 assessed the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale using confirmatory factor analysis. They evaluated five dimensions of adult attachment and clarified a theoretical relationship between SNS addiction and attachment style. Park and Oh 53 identified the key factors influencing SNS addiction in pre-service teachers, finding that anxiety attachment and avoidant attachment significantly elicit SNS addiction. Furthermore, insecure adult attachment was found to mediate the effect of covert narcissism on SNS addiction.

Social influence, broadly defined, encompasses the array of ways in which individuals change their thoughts, feelings, and behaviors as a result of interacting with others 54 . Several studies have linked the role of social influence to increasing time spent on SNS platforms. Algorithms designed to show content from close connections or popular trends create a reinforcing loop, wherein users continuously engage to stay updated and relevant 55 . Furthermore, the witnessing of peers frequently engaging with or endorsing certain content or platforms can create a normative behavior pattern, leading individuals to subconsciously conform and potentially enter into a cycle of addictive behavior 56 . While social influence plays a significant role, the extent of its impact on an individual can vary based on personal factors. Those with low self-esteem or a high need for social validation may be more susceptible to SNS addiction under strong social influence 51 . Conversely, individuals with strong personal resilience and critical media literacy might navigate SNS spaces without succumbing to addictive behaviors, despite prevalent social influences. In summary, the intricate relationship between societal impact and dependency, particularly within the domain of SNSs, presents a diverse and intricate area of exploration. As society becomes increasingly digital, comprehending these intricacies becomes crucial in fostering constructive online conduct and lessening the vulnerabilities of dependency.

Scholars have addressed the concepts of flow, perceived enjoyment, and habit to explain behaviors associated with addiction to SNSs. Flow has been observed in various digital experiences, including gaming, website browsing, and SNS usage 57 . This deep immersion can amplify the appeal of SNS, making users more prone to spend extended periods in such platforms. Researchers like Faiola et al. 58 have argued that achieving a state of flow can lead to repeated usage, which, over time, can contribute to addictive behaviors. This is because the gratification derived from such immersive experiences makes users more likely to seek them out repetitively. In the context of SNS, perceived enjoyment signifies the pleasure users derive from platform activities, be it scrolling, posting, or interacting with peers. Studies have identified perceived enjoyment as a strong predictor of SNS use 13 , 36 . Platforms that offer high levels of enjoyment can foster continued and increased usage. Over time, the search for this intrinsic pleasure can drive individuals to use SNS excessively. This compulsive need to seek out enjoyment can pave the way for addictive behavior patterns 11 . Furthermore, the daily ritual of checking and engaging with SNS can lead to the formation of strong habits 18 . With notifications and constant updates, SNS platforms are designed to encourage routine interactions, facilitating the transformation of casual usage into habitual behavior 59 , 60 . As habits become deeply ingrained, they can become automatic responses, often executed without much thought or conscious intention. This automaticity is concerning as users might find themselves compulsively checking SNS without any particular reason or prompt, signaling potential addiction 41 . Lee et al. 24 posited that SNS addiction has a significant impact on flow and user satisfaction, finding that addiction positively affects flow. Gong et al. 61 explored the factors affecting mobile SNS addiction, discovering that flow plays a crucial role in increasing the level of addiction. They also found that enjoyment, sociability, and informational value are major antecedents of flow, leading to a higher level of addiction. Seo and Ray 62 examined the effects of habit and addiction in SNS use, revealing that immersion and concern for social acceptance are significant factors in increasing addictive use. Habitual use was also shown to have a positive influence on addictive use. Focused immersion is conceptually similar to flow in this study.

Additionally, researchers have studied SNS addiction by various perspectives. Yang et al. 10 considered SNS addiction as pathological behavior in the context of mobile SNS, while high engagement was classified as non-pathological behavior. It was revealed that SNS enjoyment significantly impacts both addiction and high engagement, and that habit is significantly related to addiction. Osatuyi and Turel 30 examined the precursors of SNS addiction symptoms using the dual system theory. They found that habit affects SNS addiction symptoms both directly and indirectly. Social self-regulation was also significantly associated with SNS addiction symptoms. Pontes et al. 63 studied the role of cognitive-related factors and psychiatric distress in SNS addiction, demonstrating that fear of missing out, maladaptive cognitions, and psychiatric distress significantly contribute to addiction. Turel and Serenko 36 summarized three theoretical perspectives: the cognitive-behavioral model, the social skill model, and the socio-cognitive model. Griffiths 64 pointed out potential controversy over SNS addiction, suggesting improvements in methodological design, sample representativeness, and scale validity to bridge the gap between empirical findings.

Even with an abundance of studies focusing on these areas, a comprehensive exploration that seamlessly combines affect, social influence, flow, perceived enjoyment, and habit is noticeably absent. In light of this, our study endeavors to weave these constructs together, forging a cohesive blueprint that underscores their collective influence on SNS addiction. Through this endeavor, we aspire to deliver crucial insights to scholars and professionals alike, charting a path towards strategies that encourage healthier interactions with SNS platforms.

Research model

Figure  1 presents the research model for understanding the determinants of SNS addiction. This study elucidates the roles of flow, perceived enjoyment, and habit in leading to addiction. It proposes that positive affect, negative affect, and social influence have significant impacts on the precursors of addiction.

Research model.

Positive affect

Positive affect, as defined by Watson et al. 25 , pertains to the extent to which individuals feel active, alert, and enthusiastic. Previous research suggests a significant relationship between positive affect and flow 65 , 66 as well as perceived enjoyment 13 . In the context of SNS usage among college students, those with higher levels of positive affect may become more immersed in SNS activities to sustain these positive emotions, consequently exhibiting addictive behavior. They may experience a sense of joy due to their positive feelings and thus repetitively open the SNS app. Based on these observations, the current study posits that positive affect serves as a potent factor in the development of addiction, flow, habit, and perceived enjoyment.

Positive affect significantly influences flow.

Positive affect significantly influences perceived enjoyment.

Positive affect significantly influences habit.

Negative affect

Negative affect is characterized as a state of distress and unenjoyable engagement that encompasses a range of aversive mood states 25 . In the context of SNS use among college students, negative affect can significantly influence their engagement with these platforms. As students experience higher levels of negative affect, they may alter their flow and habits on SNS platforms in an attempt to mitigate these uncomfortable feelings. For instance, students might increase their usage of SNS as a coping mechanism to distract themselves from their negative emotions 67 , 68 . Alternatively, they could also withdraw from SNS platforms due to their decreased enjoyment derived from the platforms when in a negative mood state. Consequently, negative affect can significantly influence the flow, habit formation, and perceived enjoyment associated with SNS use.

Negative affect significantly influences flow.

Negative affect significantly influences perceived enjoyment.

Negative affect significantly influences habit.

Social influence

Social influence refers to the degree to which they are influenced by each other's actions in social relationships 69 . It includes relational norms and the identity that users feel like a member of society. Social influence significantly determines the intention to use SNS 70 , 71 . The ultimate purpose of users using SNS is to form and experience social relationships. In this context, users who are greatly influenced by their acquaintances may experience addiction and flow. The more people around users have an influence on SNS, the more users will try to use it repeatedly and enjoy it. Hence, social influence is believed to significantly affect addiction, flow, habit, and perceived enjoyment.

Social influence significantly influences flow.

Social influence significantly influences perceived enjoyment.

Social influence significantly influences habit.

Flow refers to the holistic experience that individuals feel when they act with total involvement 72 . It significantly affects addiction in several online contexts 32 , 33 . Focused immersion is positively related to the addictive use of SNS 62 . Flow has a significant association with SNS addiction 61 , 73 . Thus, one can expect that flow serves as a crucial factor in shaping addiction.

Flow significantly influences addiction.

Perceived enjoyment

Perceived enjoyment is a key intrinsic motivation for information system usage 74 . It plays a preeminent role in enhancing addiction to online behavior 33 . Perceived pleasure was also found to be significantly related to social networking addiction 75 , 76 , 77 . The more SNS users enjoy social media activities, the more they would be immersed and immersed in them. SNS enjoyment serves as the salient factor in generating addiction and habit 10 . Given the above, this study is expected to show that perceived enjoyment significantly impacts addiction, flow, and habit.

Perceived enjoyment significantly influences addiction.

Perceived enjoyment significantly influences flow.

Perceived enjoyment significantly influences habit.

Habit represents repeated patterns of behavior that occur automatically without conscious awareness 78 . Habit significantly drives the experience of addiction symptoms 30 and positively influences mobile SNS addiction 10 . Habitual use of SNS drives addictive use 62 . Hence, it is predicted that habit has a significant effect on addiction.

H6. Habit significantly influences addiction.

Methodology

This study was approved by the Institutional Review Board (IRB) of HJ Institute of Technology and Management (HJITM), ethical committee of HJITM (HJITM-IRB-22-10-0008). In accordance with the ethical guidelines provided by the committee, we ensured to obtain in written form informed consent from all the study participants. All participants were informed about the purpose and the nature of the study, their rights to anonymity and confidentiality, and their freedom to withdraw from the study at any time without penalty.

Subjects and data collection

This research focused on a specific demographic group, namely, full-time undergraduate students from various higher education institutions, due to their noted high engagement with SNS and the consequent potential for addictive behavior. Eligibility for participation in this study was based on the following criteria: participants were required to be currently enrolled full-time undergraduate students within the age range of 19–30 years. Furthermore, participants were required to exhibit active engagement with at least one Facebook, indicated by daily logins or frequent activity on the platform. Prospective participants failing to meet these established criteria were excluded from the study.

Facebook was selected as the platform of interest for this study due to several reasons. First, as of our knowledge cutoff in March 2023, Facebook remains one of the most popular and widely used SNSs globally, with billions of monthly active users 79 . This extensive user base increases the likelihood of obtaining a representative and diverse sample for the study, enhancing the external validity of our findings. Second, Facebook's comprehensive features, ranging from text updates, image and video sharing, livestreaming, private messaging, groups, events, to various interactive activities, make it a robust platform for studying diverse user behaviors. The variety of tools and features on Facebook may contribute to a higher risk of addictive behaviors as users have multiple avenues for engagement. Last, previous research on SNS addiction has frequently used Facebook as the platform for study due to its popularity and diverse user demographic. This consistency allows for easier comparison of results across different studies, thereby contributing to a more cohesive body of literature on SNS addiction.

In advance of participant recruitment, an a priori power analysis was conducted using DanielSoper calculation 80 . The results indicated a necessary minimum sample size of 200 participants for achieving an alpha level of 0.05 and a power of 0.80, thus ensuring the detection of medium-sized effects. In order to secure a representative sample, we employed a stratified sampling technique, taking care to ensure adequate representation from a variety of institutions, faculties, and academic years. An electronic questionnaire was disseminated through a range of online platforms and college forums, with a clear emphasis on informed consent and guaranteed anonymity of the respondents.

Data collection was carried out over a period of two months. During the distribution of the questionnaire, we collaborated with several professors who graciously assisted with the sampling, allowing us to target a diverse group of students from various majors and academic years. Additionally, we disseminated the survey through online communities and portals frequented by university students. By using these methods, we gathered data from a broad range of university student respondents. The instruments in questionnaire incorporated sections dedicated to the collection of demographic data, SNS usage patterns, and specific psychological variables of interest including affect, flow, habit, perceived enjoyment, and levels of SNS addiction. Following the collection of data, analysis was conducted using SPSS software. Descriptive statistics were initially employed to provide a summary overview of the collected data, with subsequent inferential statistical analyses performed to test the established research hypotheses. A total of 282 responses were used for the final analysis. Table 1 presents the demographic characteristics of the study's respondents. The total sample consisted of 282 participants. Regarding gender distribution, 127 participants (45.0%) identified as male, while 155 participants (55.0%) identified as female. This indicates a slightly higher representation of female respondents in the study. The age distribution among the respondents was grouped into three categories: those in their teens (10s), twenties (20s), and thirties (30s). A significant proportion of the participants fell within the teen and twenties age categories, with each constituting 32.3% and 35.8% of the total respondents respectively. Participants in their thirties made up the smallest age group, with 90 respondents (31.9%). It should be noted, however, that these percentages overlap due to the significant number of individuals in their early twenties who are also technically in their late teens. In summary, the respondent profile reflects a diverse and representative sample of individuals with varied demographic characteristics, which strengthens the generalizability of the study's findings.

Measurement instrument

Table 2 presents the definitions of the primary research variables utilized in the study. Positive affect and negative affect are defined based on the emotional reactions users experience when engaging with SNS, and they are sourced from Beatty and Ferrell 81 . Social influence represents the perception of the need to use SNS to stay current or due to recommendations, as identified by Li 71 . Flow, sourced from Gong et al. 61 , describes the immersive and singularly focused state of a user on SNS activities. Davis et al. 74 provides the definition for perceived enjoyment, emphasizing the pleasure and interest users derive from SNS. Habit, according to Limayem et al. 26 , is characterized by a consistent and unconscious tendency to use SNS during leisure or to alleviate boredom. Lastly, Addiction captures the intense involvement in SNS, leading to diminished real-world social interactions and a decrease in positive emotions, as described by Osatuyi and Turel 30 .

In measuring constructs in this study, all questions were adapted from previously validated studies in the information systems and social networking services fields. These items were modified to fit the context of SNS use. All indicators were assessed using a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The measurement of SNS addiction was done using self-reporting scales, comprising various items that gauge the frequency, intensity, and the negative implications of SNS use. In our research, we use the following items Osatuyi and Turel 30 : "I was immersed in SNS and experienced a decrease in conversations when meeting people.", "As I used SNS, the affectionate emotions of the past decreased." Respondents rate these statements on a scale, commonly ranging from "strongly disagree" to "strongly agree." The specific measurement items and corresponding references of all constructs can be found in Table A1 .

Prior to deployment, the questionnaire underwent a rigorous review by two information systems researchers to address content relevance and question ambiguity. Moreover, a pilot test was conducted to examine the clarity, comprehension, and applicability of the survey items. This preliminary study involved 20 participants, representative of our target population. The participants were requested to provide feedback regarding the understandability, relevance, and any ambiguity related to the survey questions. Based on their feedback, minor adjustments were made to improve the wording and sequencing of certain questions to enhance clarity and coherence.

This study validated the measurement model and the structural model by using the partial least squares structural equation modeling (PLS-SEM) through SmartPLS 4 82 . PLS-SEM also offers some benefits in terms of fewer restrictions on sample size and residuals compared to covariance-based SEM such as AMOS and LISREL 83 , 84 .

Common method bias (CMB)

To assess the potential issue of CMB, Harman’s one-factor test was employed 85 . In the exploratory factor analysis, results indicated that the first factor explained 32.801% of the variance, which was considerably less than the threshold of 50%, suggesting that CMB was not a predominant concern in our data. Moreover, in observing the variance inflation factor (VIF) from our regression analysis, all values were well below the critical value of 10, providing further evidence against significant multicollinearity and CMB 86 .

Measurement model

This study assessed the reliability and validity of the measurement model. This research examined Cronbach's alpha and composite reliability (CR: rho_A, rho_C) to evaluate reliability. If Cronbach's alpha is over 0.60 87 and CR is greater than 0.7 88 , reliability is achieved. As shown in Table 3 , Cronbach’s alpha and CR scores of all the constructs, except for social influence, exceeded the recommended value. Nevertheless, this study decided to retain social influence as other estimates such as CR (rho_C) and AVE were well above the recommended threshold (0.824 and 0.702, respectively).

This study investigated convergent validity and discriminant validity to evaluate the validity. Convergent validity was confirmed by investigating both the average variance extraction (AVE) and the factor loads of the items associated with each construct. AVE values ranged between 0.702 and 0.899 which are higher than the expected threshold of 0.5 88 . Factor loadings ranged from 0.765 to 0.964 and are all statistically significant at the p  = 0.001 levels, supporting that the model has a satisfactory level of convergent validity 89 .

Discriminant validity ensures that a construct is indeed distinct from other constructs by empirical standards 88 . For the present study, two criteria were utilized to determine discriminant validity. Firstly, the square root of the average variance extracted (AVE) for each construct was compared against its correlations with other constructs. As presented in Table 4 , the diagonal values (which are the square root of the AVE) for each construct are greater than the off-diagonal values in their respective rows and columns. This demonstrates that the constructs share more variance with their indicators than they do with any other construct, thus meeting the criteria recommended by Fornell and Larcker 88 .

Additionally, the heterotrait-monotrait ratio of correlations (HTMT) was utilized to assess discriminant validity. As suggested by the literature, an HTMT value below 0.85 indicates the presence of discriminant validity 90 . Table 5 provides the HTMT ratios for all constructs. It's clear from the values that all the ratios are below the 0.85 threshold, further confirming the discriminant validity of our constructs. In conclusion, both the square root of AVE and HTMT criteria confirm the discriminant validity of the constructs utilized in this study.

Moreover, HTMT assessment was carried out using bootstrapping with 5000 samples. All 95% confidence intervals substantially veer away from the null value of 1, thereby attesting to discriminant validity (the most elevated value observed is 0.713 between perceived enjoyment and habit; all other pairs showcase values of 0.691 or lower). At the 99% confidence intervals, the upper boundary for the pair perceived enjoyment-habit increases to 0.741, with the subsequent highest value being 0.729.

Structural model

The hypotheses were tested by using the PLS-SEM technique. This study carried out bootstrapping resampling method with 5000 re-samples. Ten of the fourteen hypotheses in the research framework are supported. Figure  2 shows the analysis results.

Analysis results.

As proposed, positive affect has a significant positive influence on both flow (b = 0.298, t = 3.709) and perceived enjoyment (b = 0.515, t = 11.56), strongly supporting H1a and H1b. Contrary to expectations, positive affect does not impact habit (b = − 0.029, t = 0.435), failing to confirm H1c. As anticipated, negative affect has a significant correlation with both flow (b = 0.239, t = 3.456) and perceived enjoyment (b = − 0.341, t = 6.907), thereby supporting H2a and H2b. Conversely, negative affect does not have an impact on habit (b = 0.101, t = 1.592), failing to support H2c. Contrary to expectations, social influence does not correlate with the flow (b = 0.059, t = 1.017), and thus, H3a is not supported. In line with the hypothesis, social influence significantly influences both perceived enjoyment (b = 0.231, t = 3.759) and habit (b = 0.214, t = 3.75), which validates H3b and H3c. As expected, flow is significantly associated with addiction (b = 0.225, t = 3.643), confirming H4. Perceived enjoyment, as hypothesized, exerts a significant positive effect on both flow (b = 0.15, t = 2.476) and habit (b = 0.496, t = 8.199), robustly supporting H5a and H5b. Contradictory to expectations, perceived enjoyment does not influence addiction (b = − 0.056, t = 0.675), failing to confirm H5c. In line with the hypothesis, habit is significantly linked to addiction (b = 0.229, t = 3.09), thereby supporting H6. Overall, the conceptual framework accounted for approximately 11.6% of the variance in addiction. Table 6 shows the summary of the SEM results.

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I Ran 4 Experiments to Break My Social Media Addiction. Here’s What Worked.

• Sarah K. Peck

Track and schedule your usage.

Are you spending too much time on social media? If you’d like to break the habit, you can try a few different techniques. One would be to quit cold turkey for a full month. If that sounds too extreme, you can avoid social media at certain times, like after dinner or before breakfast. Blocker tools like Freedom can help you stay on track. A third approach is to try a social “happy hour” — instead of staying off social media at certain times, block out a portion of every day you can look forward to indulging in it. A fourth experiment to try is a taking a day off from social every week, like a Saturday or Sunday. This “day of rest” will help you keep your social habit in check, and make the weekend feel longer.

Social media can connect us to new ideas, help us share our work, and allow previously unheard voices to influence culture. Yet it can also be a highly addictive time-sink if we’re not careful about our goals , purpose , and usage.

• SP Sarah K. Peck is an author and startup advisor based in New York City. She’s the founder and executive director of Startup Pregnant, a media company documenting the stories of women’s leadership across work and family, and host of the Startup Pregnant Podcast .

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Considering social media’s growing impact, how can we create empathetic design frameworks to improve compassion online?

As of 2021, there are over 3.78 billion social media users worldwide, with each person averaging 145 minutes of social media use per day. And in those hours spent online, we’re beginning to see the harmful impact on mental health: loneliness, anxiety, fear of missing out, social comparison, and depression. 

Social media has undoubtedly integrated itself into society, but the question remains on how to properly negotiate our relationship with it. Nina Vasan , clinical assistant professor of psychiatry at Stanford and founder and executive director at Brainstorm: The Stanford Lab for Mental Health Innovation , and Sara Johansen , resident psychiatrist at Stanford and director of clinical innovation at Stanford Brainstorm, explored possible answers to that question during a Stanford Institute for Human-Centered AI seminar  by outlining the impact of social media on mental health and psychological underpinnings of social media addiction, as well as possible opportunities to mitigate risk and promote wellbeing. Dr. Vasan and Dr. Johansen have worked with platforms such as Pinterest and TikTok to design and implement more empathic user experiences. 

What makes social media so addictive?

Variably rewarding users with stimuli (likes, notifications, comments, etc.) keeps them engaged with content. When a user’s photo receives a “like,” the same dopamine pathways involved in motivation, reward, and addiction are activated. What keeps us hooked on social media isn’t just the “pleasure rush of the like,” says Johansen, “it’s the intermittent absence of the like that keeps us engaged.” 

When does it become harmful?

One result of trapping users into endless scrolling loops is that it can lead to social comparison. When presented with the curated feeds of other people, we are vulnerable to “frequent and extreme upward social comparison,” which can lead to a number of negative side-effects such as erosion of self-esteem, depressed mood, and decreased life satisfaction. Some people try to cope with an eroded self-esteem by attacking other people’s sense of self, which can lead to cyber-bullying. 

Additionally, with advances in face tracking, facial recognition, and facial augmentation using AI, image-based apps have created questionable filters including ones designed to make a user appear more slender, which could contribute to distortions in body image. These platforms also offer “easy access to a community of people who promote and encourage disordered eating behavior,” says Vasan.

What are we doing now?

To moderate the vitriol of cyber-bullying, many companies have turned to AI as a method for classifying comments with negative sentiment and filtering them or prompting commenters to pause and reconsider their actions. 

Social media platforms are now working to ban communities that post harmful content. Many apps such as TikTok and Pinterest will present information on hotlines and support resources as a response to search queries for self-harm, suicide, depression, and eating disorder-related content. Moderation is still a complicated task as users find new ways to evade search filters, notes Vasan.

The psychiatrists don’t conclude that people must abstain completely from online platforms. For many of us, social media can be a rewarding experience that connects us with people all around the world. Instead of approaching screen time through the “displacement hypothesis,” which suggests the negative impact of technology is directly related to exposure, they recommend the “Goldilocks” hypothesis, which identifies moderate use as optimal for wellbeing.

On social media platforms, most risk mitigation methods are focused on non-maleficence, based on the principle to do no harm. Vasan and Johansen suggest that we should also consider beneficence, which is to do good. For example, Brainstorm’s recent work with Pinterest led to Pinterest Compassionate Search, which offers free therapeutic exercises on the platform in response to depression-related search terms.

What’s next?

Both psychiatrists emphasized a need for more social media-specific research, with even more granularity with respect to individual apps and not just smartphone use as a whole. 

They also recommend app makers consider more than the most simplistic business incentives. As we shift from “minimizing harm to promoting wellbeing,” Johansen says, it is important to realize that the friction associated with making apps less addictive “is going to come at a loss of some growth.” In the end it comes down to choosing that option because “it’s the ethical thing to do, because we have a responsibility to help these young minds develop in a healthy way.”

Drs. Vasan and Johansen consult for TikTok. Dr. Vasan has also consulted for Pinterest and Instagram.

Watch the full seminar:

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How to break social media addiction, or spend less time online

• You may be able to break a social media addiction by going on a cleanse, setting limits, and deleting apps.
• While you don't need to abstain from social media entirely, experts say it's important to set limits.
• This article  was medically reviewed  by  Zlatin Ivanov, MD , who is certified in psychiatry and addiction psychiatry by the American Board of Psychiatry and Neurology at  Psychiatrist NYC .
• Visit Insider's homepage for more stories .

The American Society of Addiction Medicine defines addiction as a behavior that becomes compulsive or continues despite negative consequences. In 2017, 43% of Americans reported checking social media constantly, and 20% said social media is a source of stress. 

In addition, interacting with social media can trigger a dopamine response in the brain, similar to that triggered by drug or alcohol use. That response can leave you wanting more and feeling addicted. Here's how to fight it. 

How to break social media addiction

In 2018, people with internet access worldwide spent an average of 144 minutes on social media every day. Yet research indicates that limiting social media use to 30 minutes a day is optimal for mental health.  

Abstinence is often recommended for treating drug or alcohol addiction, but for social media addiction, the ideal psychological outcome is controlled use of the internet. It's not necessary to give up social media entirely, but it is important to have strategies for setting limits. 

Lin Sternlicht , a licensed mental health counselor at Family Addiction Specialist, recommends that people who are concerned about social media addiction take the following steps:

• Go on a social media cleanse: Challenge yourself to go a certain time without checking social media, whether it's for a few hours or an entire week. One 2019 study found that some students who went for five days without social media experienced a "sense of serenity," although others were afraid of missing out. 
• Delete apps, or disable notifications from social media: Most people check into social media mindlessly, so put a small barrier in the way by turning off notifications . If you don't see a social media icon or alert every time you pick up your phone, you're less likely to spend time there. 
• Set limits and stick to them. Most phones and tablets allow you to see the time you've spent on certain apps. Set a limit for your time spent on social media and stick to it, or use an app that blocks social media after you've hit your limit. For teens , the American Academy of Pediatrics also recommends that social media use not interfere with activities like family meals, exercise, or "unplugged downtime."
• Dedicate time to hobbies or activites. A hobby or new activity can help curb your desire to check in to social media. "The idea here is to fill up your free time with things that you enjoy that are good for you," Sternlicht says. "Naturally you will find less time to be on social media and more time to be present in life, and hopefully even socialize in person instead of through a screen."

Accountability is more important than abstinence 

Going on a digital detox — or totally abstaining from social media for a certain period of time — can be effective for some people, but not others, says Neha Chaudhary , MD, child and adolescent psychiatrist at Massachusetts General Hospital and Harvard Medical School. 

"For some, it may break a cycle that has started to feel toxic or have negative effects," she says. "For others, stopping altogether may lead to craving its use and not being able to sustain the break, or might keep someone from accessing the beneficial parts of social media, like a way to stay connected and reach out for support."

Rather than relying on a total detox, Chaudhary recommends setting limits and recruiting some of your friends and family to join you. 

"Accountability plays a big role in trying to make any change," she says. "Maybe decide with a friend that you want to both reduce use, or tell your family member your goals so that they can check in with you about it. Whatever it is, find a way to have someone help keep you on track — breaking habits alone can be difficult."

In severe cases, someone who is worried about social media addiction should also consider seeking professional help from a therapist or mental health specialist.

Watch: I quit social media for 1 month — it was the best choice I ever made

• Main content

Social Medias Impact on Addiction Recovery Explored

September 5, 2024

By Arista Recovery Staff

Discover how social media impacts addiction recovery. Unravel the neural effects and strategies for healthy use.

Understanding Social Media Addiction

Delving into the realm of social media addiction, it's essential to grasp the definition and characteristics of this modern-day phenomenon and understand its prevalence in American society.

Definition and Characteristics

Social media addiction shares striking similarities with substance use disorders, encompassing traits such as mood modification, salience, tolerance, withdrawal symptoms, conflict with other activities, and relapse. These platforms trigger neural circuitry, particularly dopamine release upon positive feedback reception, fostering desires for continued engagement and validation.

Individuals experiencing social media addiction may exhibit compulsive behaviors, incessantly seeking gratification through online interactions and validation. This dependency on social media for emotional fulfillment can lead to neglect of real-life responsibilities, strained relationships, and adverse mental health consequences.

Prevalence in American Society

Statistics reveal that approximately 5 to 10% of Americans meet the criteria for social media addiction, with a notable impact on mental health and relationships, particularly among children and young adults [1] . The addictive nature of social media consumption mirrors patterns seen in substance use disorders, characterized by compulsive usage patterns and a diminished ability to control impulses.

The constant validation received through social media notifications, such as likes and mentions, triggers a dopamine rush, creating a cycle of pleasure-seeking behavior and reinforcement of the habit [2] . This repetitive reward system can lead individuals to rely on social media as a coping mechanism, especially during times of stress, loneliness, or depression, further deepening their addiction and skewing their perception of real-world interactions.

Incorporating a deeper comprehension of the definition, characteristics, and prevalence of social media addiction is essential in addressing its impact on addiction recovery and overall well-being. By raising awareness and promoting healthy online habits, individuals can navigate the digital landscape more mindfully and establish a healthier relationship with social media platforms.

Impact of Social Media Addiction

The impact of social media addiction goes beyond just screen time; it can have profound effects on mental health, social interaction skills, and body image concerns.

Mental Health Effects

Overuse of social media platforms can lead to detrimental effects on mental health, especially impacting individuals in addiction recovery. Feelings of depression, anxiety, and isolation can be exacerbated by excessive social media use. The dopamine rush triggered by social media interactions can mirror the effects of addictive substances, making it challenging for individuals in recovery to maintain healthy habits and behaviors [3] .

Real-world human connection is essential for alleviating stress and promoting happiness, health, and positivity. However, social media can often act as a barrier to forming these genuine relationships, hindering the emotional support needed for successful recovery. For more information on breaking the cycle of addiction within families, visit our article on breaking the cycle of addiction in families .

Social Interaction Skills

One significant impact of social media addiction is the stunted development of social interaction skills. Constant reliance on digital communication can hinder the ability to engage in face-to-face interactions, which are crucial for building meaningful relationships and support networks, especially during the recovery process [3] .

Individuals in addiction recovery may find it challenging to cultivate real-world connections and communities when social media serves as a distraction from forming genuine bonds. Engaging with online communities that promote substance use can pose a significant challenge, potentially triggering cravings and hindering the recovery journey. Exploring realistic films about addiction can provide insight and support for those facing similar struggles; check out our article on realistic films about addiction for more information.

Body Image Concerns

Social media platforms often perpetuate unrealistic beauty standards and curated lifestyles, leading to body image concerns among users. Exposure to idealized and unattainable body images can fuel feelings of inadequacy and contribute to issues related to self-esteem and self-worth.

For individuals in addiction recovery, these body image concerns can be particularly challenging, as they may already be grappling with issues related to self-acceptance and identity. It's crucial to address these concerns and work towards fostering a positive self-image through self-care practices and seeking support from professionals or support groups. To explore how pets can aid in the recovery process and provide emotional support, refer to our article on how pets can help with the recovery process .

Neural Effects of Social Media

Unraveling the intricate web of addiction and its connection to social media reveals the profound neural effects that can ensue. Understanding the dopamine release mechanism intertwined with social media engagement and drawing a comparison to substance use illuminates the depth of this impactful relationship.

Dopamine Release Mechanism

Social media platforms are cunningly designed to activate the same neural circuitry as gambling and recreational drugs, leading to the release of dopamine in the brain upon interactions such as receiving notifications or positive feedback. This surge of dopamine fuels a cycle of reward and reinforcement, compelling individuals to seek more engagement, mirroring addictive behaviors [1] .

This dopamine-induced response can establish a sense of salience, tolerance, and even withdrawal symptoms, akin to what is observed in substance use disorders. The allure of social media stimuli can trigger a significant dopamine release, fostering a habitual engagement pattern that parallels addictive tendencies, contributing to the captivating yet potentially harmful nature of social media addiction.

Comparison to Substance Use

The addictive potential of social media is further underscored by the parallels it shares with substance use. Research has revealed that heavy use of social media is linked to a heightened risk of mental health challenges such as depression, anxiety, loneliness, self-harm behaviors, and even suicidal ideation, emphasizing the detrimental impact social media can have on mental well-being and its potential addictive nature.

Platforms like TikTok, Facebook, Snapchat, and Instagram foster dopamine-inducing social environments that can trigger the brain's reward center similarly to substances like cocaine. This alignment exposes the alluring and addictive nature of social media interactions, shedding light on the profound influence these platforms wield over individuals' behaviors and emotional responses [6] .

The exposure to substance-related content on social media adds another layer of complexity to the challenge of addiction recovery. Such exposure can reignite cravings, romanticize substance use, and amplify temptations, making it arduous for individuals in recovery to resist triggers and maintain their progress. This underscores the pivotal role social media plays in either supporting or hindering the recovery journey, accentuating the need for strategies that foster healthy online interactions and minimize triggers [7] .

By probing into the neural effects of social media, particularly the dopamine release mechanism and its resemblance to substance use patterns, we can comprehend the intricate interplay between social media engagement and addictive behaviors, paving the way for informed strategies to address and navigate this evolving landscape of addiction recovery in the digital age.

Connection to Substance Use

The connection between social media use and substance use has become increasingly evident, especially among young adults. The correlation between these two factors, particularly among women, is noteworthy. Spending more time on social media platforms has been associated with an increase in substance use behaviors.

Correlation Among Young Adults

Young adults, especially women, exhibit a significant correlation between their social media habits and substance use patterns. The influence of social media on behaviors related to substance use is a growing concern in this demographic. Studies have shown that the more time individuals spend on social media each day, the higher the likelihood of engaging in substance use activities.

Increased Substance Use

Excessive social media consumption can lead to increased feelings of loneliness and inadequacy, as highlighted by APN Resources . Despite the platforms being designed for connectivity, heavy use can paradoxically result in feelings of isolation, pushing individuals towards seeking comfort or escapism in alcohol and other substances. This pattern can potentially lead to the development of dependency issues related to substance use.

It's crucial to recognize the impact that social media engagement can have on behaviors related to substance use. By understanding the correlation between social media use and substance use, individuals can take proactive steps to manage their online interactions and mitigate the risk of falling into patterns of addictive behaviors. For more insights on breaking the cycle of addiction within families, explore our article on breaking the cycle of addiction in families .

Challenges in Addiction Recovery

Navigating the landscape of social media addiction poses unique challenges to individuals in addiction recovery. Two critical challenges faced are the emotional nuance in online interactions and the lack of empathy and compassion often experienced in virtual environments.

Emotional Nuance in Online Interactions

When individuals engage in social media platforms, they may encounter challenges related to emotional nuance. The lack of face-to-face communication can hinder the full understanding of emotional cues and tones present in personal interactions. This deficiency in emotional nuance can lead to misinterpretations, misunderstandings, and miscommunications, impacting the ability to establish genuine connections.

In the realm of addiction recovery, where emotional support and understanding play pivotal roles, the absence of emotional nuance in online interactions can pose significant obstacles. Individuals may struggle to express their feelings authentically, receive adequate emotional support, or interpret the emotions of others accurately, hindering the recovery process.

Lack of Empathy and Compassion

Moreover, the digital nature of social media platforms can contribute to a lack of empathy and compassion in online interactions. The virtual environment often fosters a sense of detachment and anonymity, diminishing the natural human inclination towards empathy and compassion. Without the immediate presence of individuals and real-time emotional cues, it can be challenging for empathy and compassion to fully resonate through digital communication.

In the context of addiction recovery, where empathy, compassion, and understanding are fundamental to healing and growth, the absence of these elements in online interactions can impede progress. Individuals may struggle to receive the emotional support they need, feel disconnected from their support networks, or encounter insensitivity and misjudgments in virtual interactions, affecting their overall well-being.

Addressing the challenges posed by emotional nuance and the lack of empathy and compassion in online interactions is essential for individuals navigating addiction recovery in the digital age. By recognizing these barriers and actively seeking ways to foster genuine connections, promote empathy, and cultivate compassion both online and offline, individuals can enhance their recovery journey and build a strong support system that transcends the digital divide.

Strategies for Healthy Social Media Use

When it comes to navigating social media in the context of addiction recovery, adopting strategies for healthy usage is vital for maintaining progress and well-being. Two key strategies to consider are avoiding triggers and temptations, as well as balancing online and offline interactions.

Avoiding Triggers and Temptations

Social media platforms can often be a double-edged sword for individuals in addiction recovery. While they can provide a sense of community and support, they can also expose users to triggering content that may jeopardize their progress [1] . To mitigate the risk of relapse, it's essential to identify and avoid triggers and temptations on social media.

One effective strategy is to curate your social media feed by unfollowing accounts or pages that promote substance use or glamorize addictive behaviors. Instead, follow accounts that provide positive and uplifting content related to recovery and well-being. Utilize platform tools that allow you to block or filter specific content that may be triggering.

Additionally, set boundaries around your social media usage. Allocate specific time slots for engaging with social media and prioritize real-world interactions and activities. Practicing mindfulness and being aware of how social media influences your emotions and behaviors can help you better manage triggers and cravings.

Balancing Online and Offline Interactions

While social media can be a valuable tool for staying connected and seeking support, it's essential to strike a balance between online and offline interactions for overall well-being. Spending excessive time on social media can lead to feelings of isolation, FOMO (Fear of Missing Out), and negatively impact mental health.

To maintain a healthy balance, prioritize face-to-face interactions with friends, family, and support groups. Engage in activities that promote physical well-being and mental health, such as exercise, hobbies, and practicing mindfulness. Make a conscious effort to spend quality time away from screens and immerse yourself in the present moment.

When utilizing social media for connection and support in addiction recovery, be selective about the content you engage with and the communities you participate in. Seeking out accounts and hashtags that align with your recovery journey can provide a positive and supportive online environment [8] . Remember that social media should complement your recovery efforts, not replace them. By finding a healthy balance between online and offline interactions, you can harness the benefits of social media while safeguarding your well-being and progress in addiction recovery.

[1]: https://www.coniferpark.com/blog/social-media-impacts-addiction-recovery

‍ [2]: https://www.addictioncenter.com/behavioral-addictions/social-media-addiction/

‍ [3]: https://apn.com/resources/social-media-recovery/

‍ [4]: https://www.helpguide.org/mental-health/wellbeing/social-media-and-mental-health/

‍ [5]: https://www.helpguide.org/mental-health/wellbeing/social-media-and-mental-health

‍ [6]: https://www.addictioncenter.com/behavioral-addictions/social-media-addiction

‍ [7]: https://wishrehab.com/blog/social-medias-impact-on-addiction-recovery-staying-connected-without-triggering-a-relapse

‍ [8]: https://www.addicted.org/news/the-pros-and-cons-of-social-media-when-in-addiction-recovery/

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Health benefits and adverse effects of kratom: a social media text-mining approach.

1. Introduction

2. materials and methods, 2.1. screening relevant literature, 2.2. search query development, 2.3. data collection and preprocessing, 2.4. topic identification and validation, 3.1. topic identification and validation—kratom benefits, 3.2. topic identification and validation—kratom adverse effects, 4. discussion, 4.1. kratom benefits, 4.2. kratom adverse effects, 4.3. linking findings to research questions and hypotheses, 4.4. implications for the field and practical applications, 4.5. limitations and future research, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, appendix a. example posts for kratom benefits identified through topic-mining.

Appendix B. Example Posts for Kratom Adverse Identified through Topic-Mining

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• Singh, D.; Murugaiyah, V.; Hamid, S.B.S.; Kasinather, V.; Chan, M.S.A.; Ho, E.T.W.; Grundmann, O.; Chear, N.J.Y.; Mansor, S.M. Assessment of Gonadotropins and Testosterone Hormone Levels in Regular Mitragyna Speciosa (Korth.) Users. J. Ethnopharmacol. 2018 , 221 , 30–36. [ Google Scholar ] [ CrossRef ]
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• Wahbeh, A.; Nasralah, T.; El-Gayar, O.; Al-Ramahi, M.; Elnoshokaty, A. Adverse Health Effects of Kratom: An Analysis of Social Media Data. In Proceedings of the Hawaii International Conference on System Sciences, Kauai, HI, USA, 5–8 January 2021. [ Google Scholar ]
• Naslund, J.A.; Bondre, A.; Torous, J.; Aschbrenner, K.A. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J. Technol. Behav. Sci. 2020 , 5 , 245–257. [ Google Scholar ] [ CrossRef ]
• Choi, J.; Yoon, J.; Chung, J.; Coh, B.-Y.; Lee, J.-M. Social Media Analytics and Business Intelligence Research: A Systematic Review. Inf. Process. Manag. 2020 , 57 , 102279. [ Google Scholar ] [ CrossRef ]
• Mayo Clinic Kratom: Unsafe and Ineffective. Available online: https://www.mayoclinic.org/healthy-lifestyle/consumer-health/in-depth/kratom/art-20402171 (accessed on 23 April 2022).
• MedicalNewsToday Side Effects: Medication, Types of Effect, Cancer Treatment. Available online: https://www.medicalnewstoday.com/articles/196135 (accessed on 23 April 2022).
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Wahbeh, A.; Al-Ramahi, M.; El-Gayar, O.; Nasralah, T.; Elnoshokaty, A. Health Benefits and Adverse Effects of Kratom: A Social Media Text-Mining Approach. Informatics 2024 , 11 , 63. https://doi.org/10.3390/informatics11030063

Wahbeh A, Al-Ramahi M, El-Gayar O, Nasralah T, Elnoshokaty A. Health Benefits and Adverse Effects of Kratom: A Social Media Text-Mining Approach. Informatics . 2024; 11(3):63. https://doi.org/10.3390/informatics11030063

Wahbeh, Abdullah, Mohammad Al-Ramahi, Omar El-Gayar, Tareq Nasralah, and Ahmed Elnoshokaty. 2024. "Health Benefits and Adverse Effects of Kratom: A Social Media Text-Mining Approach" Informatics 11, no. 3: 63. https://doi.org/10.3390/informatics11030063

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• v.21(10); 2019 Oct

A Web-Based Intervention for Social Media Addiction Disorder Management in Higher Education: Quantitative Survey Study

Huseyin dogan.

1 Bournemouth University, Bournemouth, United Kingdom

Helmi Norman

2 Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, Malaysia

Amen Alrobai

3 King Abdulaziz University, Jeddah, Saudi Arabia

Norazah Nordin

Anita adnan.

4 Doctorate Support Group, Kuala Lumpur, Malaysia

Social media addiction disorder has recently become a major concern and has been reported to have negative impacts on postgraduate studies, particularly addiction to Facebook. Although previous studies have investigated the effects of Facebook addiction disorder in learning settings, there still has been a lack of studies investigating the relationship between online intervention features for Facebook addiction focusing on postgraduate studies.

In an attempt to understand this relationship, this study aimed to carry out an investigation on online intervention features for effective management of Facebook addiction in higher education.

This study was conducted quantitatively using surveys and partial least square-structural equational modeling. The study involved 200 postgraduates in a Facebook support group for postgraduates. The Bergen Facebook Addiction test was used to assess postgraduates’ Facebook addiction level, whereas online intervention features were used to assess postgraduates’ perceptions of online intervention features for Facebook addiction, which are as follows: (1) self-monitoring features, (2) manual control features, (3) notification features, (4) automatic control features, and (5) reward features.

The study discovered six Facebook addiction factors (relapse, conflict, salience, tolerance, withdrawal, and mood modification) and five intervention features (notification, auto-control, reward, manual control, and self-monitoring) that could be used in the management of Facebook addiction in postgraduate education. The study also revealed that relapse is the most important factor and mood modification is the least important factor. Furthermore, findings indicated that notification was the most important intervention feature, whereas self-monitoring was the least important feature.

Conclusions

The study’s findings (addiction factors and intervention features) could assist future developers and educators in the development of online intervention tools for Facebook addiction management in postgraduate education.

Introduction

Addiction is usually associated with addictive behavior and compulsive engagements of stimuli, such as a psychoactive chemical (eg, alcohol and drugs), despite harmful consequences. Nevertheless, behavioral addiction related to nonconsumption of substances, such as digital addiction, has recently become a topic of much interest. To date, the only psychiatric disorders that have been formally recognized are pathological gambling and internet gaming disorder [ 1 , 2 ]. As such, there is an urgent need for further research in terms of behavioral addiction [ 3 ]. As social media has become an essential platform for online communication, several studies have investigated its behavioral effects on excessive usage. Although some researchers have addressed general digital and internet addiction [ 4 ] and its psychological effects on loneliness, anxiety, and depression [ 5 ], other researchers have focused on addiction of social networking sites (SNSs) such as Facebook [ 2 , 5 ].

As of June 2018, current statistics have indicated that Facebook is the most popular social network worldwide, with over 2 billion monthly active users. Despite its benefits for the Web-based social communication and content consumption, some users develop an excessive usage of Facebook, causing potential negative effects, termed as Facebook addiction disorder [ 2 , 5 ]. Facebook addiction is defined as an addictive behavior caused by an uncontrollable level of accessing and using Facebook, which negatively affects other face-to-face social activities, studies, jobs, interpersonal relationships, and physical health [ 6 , 7 ].

Facebook addiction disorder is categorized by psychological factors such as salience, tolerance, mood modification, relapse, withdrawal, and conflict [ 8 ]. Salience is related to the mental state of continuously thinking about Facebook, whereas tolerance is related to the tolerance level of Facebook usage (eg, increase the time spent on Facebook to reach to the same effect that was initially experienced using Facebook). Mood modification is associated with whether Facebook affects current moods of the user, and relapse is linked with failed attempts of Facebook usage reduction. Meanwhile, withdrawal and conflict are related to negative conditions and effects because of failure in accessing Facebook, in which withdrawal is associated with negative conditions such as becoming restless because of failure in accessing Facebook, whereas conflict is linked with negative effects such as Facebook causing negative impacts on individuals’ current academic or professional life [ 2 , 9 ].

Previous research has revealed that Facebook addiction have caused negative psychological effects such as emotional problems, relational problems, health-related problems, and performance problems [ 9 ]. In terms of emotional problems, Facebook addiction has been revealed to cause negative mood alterations such as depression and anxiety [ 10 ], development of deficient self-regulation [ 11 ], as well as task avoidance and procrastination [ 12 ]. With regard to relational problems, Facebook addicts have experienced negative relationships in terms of family conflicts, impaired concentration at work or school, and problematic peer relationships, thus contributing to interpersonal relationship detriment [ 9 , 13 ]. With regard to health-related problems, Facebook addiction has also been associated with sleep difficulties such as insomnia and somatic problems as well as poorer sleep quality [ 9 , 14 ]. Meanwhile, for performance problems, addiction to Facebook has caused job losses and negative effects of self-reported work performance [ 9 , 14 ].

Facebook addiction has also been reported to affect higher education studies. In Turkey, a study on the effect of Facebook addiction on gender was carried out with 1257 Turkish university students [ 15 ]. The study’s findings revealed that male students had higher SNS addiction levels as compared with female students. In Poland, Facebook addiction was studied among 1157 students [ 6 ]. They discovered that Facebook addiction among Polish students was related to higher extraversion, narcissism, loneliness, and social anxiety and lower general self-efficacy. They also discovered that Facebook addiction was further related to impoverished well-being that included impaired general health, decreased sleep quality, and higher perceived stress. In the United States, an investigation was conducted with 274 university students in a statistics course, in which they examined the time distortion of social media addiction in at-risk students by intervention strategies such as prevention of Facebook use and self-control strategies [ 16 ]. They discovered that the at-risk group showed a significant upward time estimate bias when positively correlated with Facebook addiction scores. In Malaysia, Facebook addiction motives were studied with 380 postgraduates and undergraduates and the study revealed that motives that contribute to addiction are factors such as social interaction, passing time, entertainment, companionship, and communication. In another study, 441 Malaysian students were assessed on their addiction to Facebook [ 11 ]. They found out that factors such as religion, level of income, ego strength, and locus of control do not show significant influence on the risk of Facebook addiction, whereas time spent on Facebook contributed to higher addiction levels [ 17 ]. Nevertheless, these studies only mostly focused on motives of Facebook addiction level, and this shows that there is a lack of studies on the Web-based intervention systems for Facebook addiction.

Therefore, the main aim of this study was to investigate features of the Web-based intervention systems on management of Facebook addiction in postgraduate education. Considering the lack of knowledge on the development of the Web-based intervention features for Facebook addiction, the study was exploratory in nature [ 2 , 18 ]. The second aim was to investigate which intervention feature was most and least important for the management of addiction to Facebook. This could contribute to a better understanding of addiction prevention and therapeutic interventions of Facebook addiction. In the study, Web-based intervention features focused on features such as manual monitoring, manual limit, automatic notification, automatic limit, and automatic reward for learners to manage Facebook addiction. These features can be linked to an upcoming tool that is going to be introduced by Facebook called “Your Time on Facebook,” which allows for management of time on Facebook and includes an activity dashboard, a daily reminder, and management of notifications [ 19 ].

Participants

The participants of the survey were 200 postgraduates from a postgraduate support Facebook group called Doctorate Support Group (DSG). DSG is a support group that aims to provide a platform in supporting postgraduates to exchange ideas, expertise, and experiences in pursing their postgraduate education. The community has over 14,700 users who consist of postgraduates carrying out their studies and ex-postgraduate students who have completed their studies. The Facebook group applied a community of practice approach, in which the Facebook group provides a shared knowledge bank developed by the community members who have been associated with the community for a longer period. As newcomers join the group, the newcomers would learn from the old-timers who serve as coaches or mentors for the community. By participating in new activities and contributing to the community, the newcomers develop a new mastery of understanding and thus become recognized members (and later coaches) to give back to the community [ 3 , 20 ].

Data Collection and Analysis Approaches

The data were collected via administration of online surveys on Facebook group of the DSG. The online survey included mock interfaces (ie, high-fidelity prototypes) of the intervention features. This includes Facebook features as reported by Facebook in their upcoming tool called “Your Time on Facebook” [ 19 ]. These upcoming features allow for Facebook users to manage their time on Facebook, which includes an activity dashboard, a daily reminder, and management of notifications. As such, in investigating the possible Web-based intervention features for Facebook addiction during postgraduate studies, mobile phone addiction intervention features by Lee et al were adapted, which included manual monitoring, manual limit, automatic notification, automatic limit, and automatic reward features as shown in Table 1 [ 21 ]. In prevention of excessive Facebook usage, 2 features were assessed, which were manual monitoring and manual control. The manual monitoring feature investigated whether postgraduates perceived features such as manually monitoring their usage patterns (including usage time, frequency, locations, and mood) could help in Facebook addiction management. Meanwhile, the manual limit investigated whether features such as manual limiting Facebook usage based on time, location, Facebook features, and moods could potentially assist them in intervention of Facebook addiction. With regard to automatic notification, the Web-based features touched on interventions with regard to automatic notification of excessive Facebook usage based on location, features, time, and mood. The automatic limit feature is linked to features that automatically limit the usage based on time, location, frequency of use, and mood. The final feature (automatic reward), which is related to Web-based intervention features, is related to rewarding mechanisms based on usage duration and frequency, location, and mood.

The constructs and respective indicators of the Web-based intervention features for Facebook addiction disorder.

a IF: intervention Web-based feature.

The study also investigated aspects of Facebook addiction during postgraduates’ studies using the Bergen Facebook Addiction Scale, which categorizes Facebook addiction disorder by psychological factors such as salience, tolerance, mood modification, relapse, withdrawal, and conflict [ 8 ]. As discussed before, salience is related to the mental state of continuously thinking about Facebook, whereas tolerance is related to tolerance level of Facebook usage. Mood modification is associated with whether Facebook affects current moods of the user, and relapse is linked with failed attempts of Facebook usage reduction, whereas withdrawal and conflict are related to negative conditions and effects because of failure in accessing Facebook [ 2 , 9 ]. Basic demographical data (ie, gender, age, and device usage on Facebook; current experience using Facebook; and Facebook usage frequency) were also assessed in the study.

The questionnaire developed based on Bergen Facebook Addiction Scale and Web-based intervention features (manual monitoring, manual limit, automatic notification, automatic limit, and automatic reward) was run through a content validation procedure to increase its level of validity. It was validated by 2 social networking analysis experts, 2 information technology experts, and a language lecturer. The content validation involved validation on aspects such as subject matter, technology, language, and measurement. This was conducted to verify that the questions for each variable were clear and concise. As a result, the online survey consisted of 48 items as measurements. The ethics committee of the Universiti Kebangsaan Malaysia approved the implementation of this study. We followed all national regulations and laws regarding human subjects’ research and obtained the required permission to conduct this study. Participants provided Web-based informed consent to participate.

The data collected were analyzed using partial least square-structural equation modeling (PLS-SEM). This allowed for exploratory investigation of the relationship between Web-based intervention features for Facebook addiction and aspects of Facebook addiction [ 22 ]. As the study implements exploratory research in nature, PLS-SEM was chosen. The technique allows for conducting predictions and explanation of target constructs rather than confirmatory analysis with the capability of small samples sizes and complex models. PLS-SEM analysis does not make any assumptions about underlying data; thus, 200 Web-based participants are enough [ 21 , 22 ]. Hence, the study focuses only on describing the structural model analysis results (via PLS-SEM model diagrams) with regard to loadings of each construct and does not report on measurement model analysis results. The results of loadings would help in understanding the most and least important intervention features for Facebook addiction and the strongest and weakest causes of Facebook addiction among postgraduates. The software used to run PLS-SEM analysis is SmartPLS version 3.2.7 by SmartPLS GmbH. The model used in the analysis is illustrated in Figure 1 .

Model used for partial least square-structural equation modeling analysis for investigating the Web-based intervention features of Facebook addiction disorder.

Demographical Findings

Most of the 200 participants of the online survey were female (77.5%, 155/200), whereas the remaining 22.5%, 45/200 were male. The age range was quite diverse as it included individuals aged between 23 years and 51 years. For devices used to access the Facebook, 9%, 18/200 of them used only computers, 36.5%, 73/200 used only mobile phones, and 53.5%, 107/200 of them used both computers and mobile phones. The survey also gained data regarding current experience of Facebook usage. The data revealed that 86.5%, 173/200 of them have used Facebook for more than 4 years, 11.5%, 23/200 of them have used Facebook for 3 to 4 years, whereas 2.0%, 4/200 of them have used Facebook for 1 to 2 years. In addition, Facebook usage frequency was also obtained. Findings indicated that most of them (83.0%, 166/200) access Facebook every day, whereas the others access it either 2 to 3 times a week or 4 to 5 times a week. Findings also revealed that 21.5%, 43/200 of them access Facebook more than 10 times a day, 20.0%, 40/200 access it 7 to 10 times a day, 24.0%, 48/200 access it 4 to 7 times a day, and 34.5%, 69/200 of them access it once a day.

Results on Partial Least Square-Structural Equational Modeling Results: Facebook Addiction

The findings of the measurement model analysis showed that the indicators of the constructs achieved internal consistency reliability, convergent validity, and divergent validity. The results are summarized in Tables 2 - ​ -4. 4 . The findings of the structural measurement model analysis revealed that there are 6 addiction factors that are related to Facebook addiction in postgraduate studies, as shown in Figure 2 . They are mood modification, withdrawal, tolerance, salience, conflict, and relapse, which received loadings of 0.5 and above [ 22 ]. This indicates that all the indicators (eg, FB_Salience1) are related to their respective constructs (eg, FB_Salience). These results corroborate with the works of Griffiths, Kuss and Griffiths, and Andreassen, where the studies revealed that these 5 levels contribute to SNS addiction, in this case, Facebook addiction [ 9 , 22 , 23 ].

Internal consistency reliability results.

a FB: Facebook addiction factor.

b IF: intervention Web-based feature.

Divergent validity results (Fornell-Larcker Criterion).

c Not applicable.

Bootstrapping results of the partial least square-structural equation modeling analysis.

Convergent validity results.

The results also indicated that the Facebook addiction construct with the highest loading was relapse (0.666), followed by conflict (0.593), salience (0.559), tolerance (0.542), and withdrawal (0.511), as in Table 5 . The lowest loading was obtained by the mood modification construct that was 0.394. The results signify that the strongest Facebook addiction factors in postgraduates’ studies are relapse and conflict, whereas the 2 weakest levels are mood modification and withdrawal. This contradicts the results of the studies by Koc and Gulyagci as well as Balakrishnan and Shamim, where the former authors revealed that mood modification and conflict are the most frequent symptoms of Facebook addictive usage among university students, whereas the latter authors revealed that salience, loss of control, and withdrawal are the main indicators of Facebook addiction among students [ 24 , 25 ].

Structural model results.

b P <.05.

c IF: intervention Web-based feature.

These results could be caused by the fact that Facebook addiction factors could potentially be explained by a process in which a Facebook addict goes through levels of addictions that ends with relapse and conflict levels, where they attempt to reduce Facebook time but fail to do so (relapse) and ignore their studies and people (conflict) [ 9 ]. This can also be related to Facebook usage frequency of postgraduates in this study, where most of them (83%) accessed Facebook every day and 65.5% of them accessed Facebook more than 4 times a day. In addition, students who are Facebook addicts have possibly never deactivated their accounts before showing their high Facebook addiction level [ 25 ]. Furthermore, Cabral reported that the majority of participants in their study reported failed attempts of social media usage reduction [ 26 ].

The findings also revealed that 2 of the relapse construct’s indicators FB_Relapse2 and FB_Relapse2 obtained the highest loadings. The indicators were related to relapse in decision making and actions on Facebook usage, which included “decided to use Facebook during your postgraduate studies less frequently, but not managed to do so” and “tried to cut down on the use of Facebook during your postgraduate studies without success.” This is in line with the findings of Brailovskaia and Margraf’s study that investigated Facebook addiction disorder among German students [ 2 ]. They discovered that Facebook addiction factors fully mediated the association between narcissism and stress systems, and the highest positive association was with 3 factors, which were relapse, withdrawal, and salience. From that study, they revealed that users who are narcissist tend to spend more time thinking about Facebook because of Web-based self-presentation, interaction, and reflections in the social networking platform, thus causing them to be vulnerable to Facebook addiction and be in a state of relapse.

Discussion on Partial Least Square-Structural Equational Modeling Results: Web-Based Intervention Features

The findings of the structural measurement model analysis show that 6 Web-based intervention features are related to Web-based intervention and Facebook addiction in postgraduate studies. The factors are manual monitoring feature, manual limit feature, automatic notification feature, automatic limit feature, and automatic reward feature, which obtained loadings above the 0.5 cut-off point for loadings, as shown in Tables 2 - ​ -4 4 [ 22 ]. This indicates that all the indicators (eg, IF_manual_monitoring1) are related to their respective constructs (eg, manual monitoring).

The results also revealed that the Web-based intervention feature for postgraduate education that received the highest loading was automatic notification feature (0.797), followed by automatic limitation feature (0.721), automatic reward feature (0.668), and manual limitation feature (0.664). The lowest loading gained was by manual monitoring feature (0.599), as shown in Table 6 . The results suggest that the 5 intervention features could be used in management or intervention of Facebook addiction in postgraduate education. In other words, this indicates that postgraduates prefer to be notified of their Facebook usage (notification) and then be automatically managed or restricted to Facebook based on time, frequency, and location of Facebook usage as well as mood during Facebook access. Although this may seem like a straightforward solution in managing Facebook addiction, it may not be the case. This can be related to a study on Facebook addiction with regard to active Facebook use (ie, using Facebook for communication) and passive Facebook use (ie, using Facebook to consume content) [ 5 ]. They discovered that passive Facebook use was related to daily life events. Interestingly, the study revealed that participants of the study increased Facebook usage following positive life events instead of negative ones. In other words, passive Facebook use is less likely to be associated with escapism as users have decreased level of passive Facebook use when faced with problems as compared with positive experiences.

Coefficient of determination ( R 2 ) test.

This can further be related to another relevant study, where the study indicated that Facebook addiction is related to narcissism and stress systems [ 2 ]. Linking the 2 studies together, this indicates that postgraduates who have Facebook addicts are more likely to use Facebook to consume information-related positive life events, in this case related to academic success, rather than using Facebook for escapism related to negative emotions. On that note, it would be interesting for future Web-based intervention features to include the option for passive and active Facebook use and relate it with positive and negative life events in postgraduate education. In terms of the automatic reward feature, this suggests that rewards (eg, rewards systems in gaming, such as scores, or virtual currencies—refer to Yen’s study [ 27 ]) could be used as an intervention measure for addicts. Although results revealed that manual control and self-monitoring were the least important intervention features, both are still essential as they allow postgraduates to monitor their Facebook usage levels and manually control/manage Facebook features based on time, location, and feature usage as well as inputting their moods.

Conclusions, Implications, and Future Directions

The study discovered 6 Facebook addiction factors (relapse, conflict, salience, tolerance, withdrawal, and mood modification) and 5 intervention features (notification, auto-control, reward, manual control, and self-monitoring) that could be used in management of Facebook addiction in postgraduate education. The study also revealed that relapse is the most important factor and mood modification is the least important factor. Furthermore, findings indicated that notification was the most important intervention feature, whereas self-monitoring was the least important feature. This study’s findings, with regards to social media addiction factors and Web-based intervention features, could assist future developed and educators in the development of Web-based intervention tools for Facebook addiction management in postgraduate education. In addition, PLS-SEM was used as a statistical approach to verify the relationship between social media addiction disorder management and Web-based intervention features, which contributes to the field in terms of the higher education field, particularly in postgraduate education.

Future directions in this area are as follows. First, the addiction factors and intervention features were only tested in postgraduate educational settings. It would be interesting to investigate whether the findings corroborate or contradict with these findings in other educational settings, which include undergraduate, primary, and secondary education as well as long-life learning settings [ 28 ]. Second, most of the respondents were studying in local higher education institutions. It would be worth replicating the study with a larger sample with a more diverse span of international higher educational institutions [ 29 , 30 ]. Finally, it would be interesting to combine the results with social network analysis as to indicate whether social network patterns (in egocentric diagrams) could be used in the management of Facebook addiction [ 3 ] as well as other Web-based approaches [ 31 - 36 ].

Acknowledgments

This research is funded by the European Union Erasmus Mundus Action 2 Techno II Project, the Malaysian Research Universities Network (MRUN) Translational Program Grant (Grant No: MRUN-RAKAN RU-2019-003/2), and FPEND Research Grant (Grant No: GG-2019-064).

Abbreviations

Conflicts of Interest: None declared.