Internet and Social Media Addictions in the Post-Pandemic Era: Consequences for Mental Well-Being and Self-Esteem

Abstract

The increasing integration of digital technologies into daily life, particularly during the COVID-19 pandemic, has raised concerns about internet and social media addictions and their potential impact on mental health. This study aimed to examine the prevalence of internet and social media addictions among adults in Cyprus in the post-pandemic period and explore their relationship with demographic characteristics, mental well-being (i.e., depression, anxiety, and stress), and self-esteem. Participants included 502 adults from Cyprus recruited using the convenience and snowball sampling methods. The data were collected via an internet-based questionnaire that examined participants’ levels of internet addiction, social media addiction, mental well-being, and self-esteem. The results suggest that (a) while the prevalence of severe addiction was low for both internet and social media addictions, approximately one-third of participants exhibited mild to moderate levels of addiction; (b) younger adults, particularly those between 18 and 28 years of age, were more prone to problematic online behaviors; (c) internet and social media addictions were positively associated with depression, anxiety, and stress and negatively associated with self-esteem; and (d) stress was found to be a significant predictor of both internet and social media addictions, while self-esteem acted as a protective factor against problematic use. These findings highlight the enduring mental health implications of increased digital engagement and emphasize the need for targeted interventions to promote healthy online behaviors, manage stress, and enhance self-esteem.

1. Introduction

Digital technologies are one of the most transformative changes in modern history, becoming deeply ingrained in our daily lives. The internet and social media have grown considerably, becoming essential tools for communication, entertainment, work, and education (Agarwal et al. 2009; Gleason and von Gillern 2018; Hoffman et al. 2004; Oprea 2014). This shift was particularly profound during the COVID-19 pandemic when digital engagement reached unprecedented levels due to social distancing measures, lockdowns, and a shift to online platforms for professional and personal interactions. Reports indicate that internet usage increased during the pandemic, and social media platforms reported a significant increase in active users (Statista 2023b). While the internet and social media provide numerous benefits, such as instant communication and access to information, they also raise concerns about overuse and their potential adverse effects. Excessive engagement can lead to problematic behaviors, including internet or social media addiction, which have been linked to mental health challenges such as anxiety, depression, and low self-esteem (Besalti and Satici 2022; Budd et al. 2020; Masaeli and Farhadi 2021; Ozturk and Ayaz-Alkaya 2021; Sujarwoto et al. 2023). Understanding these impacts in the post-pandemic context is essential for better understanding the long-term implications of increased digital engagement.

Addiction to social media parallels internet addiction as they share the same symptoms, including obsessive thoughts about the usage of social media, mood modification to manage emotions, increased tolerance, the need for greater usage, the pursuit of pleasure, withdrawal symptoms, conflicts when spending less time on work or social relationships, and relapse after attempts to reduce or stop usage (Griffiths 1996, 2000b). The literature suggests that various forms of technological addiction are positively correlated (Kuss et al. 2014), with internet addiction seen as an umbrella term encompassing social media addiction, electronic gaming, online relationships, online shopping, and surfing, amongst others (Grover et al. 2019; Young 1998a, 1998b). Specifically, social media addiction is characterized by three main characteristics: (a) obsessive engagement, (b) the excessive need to use social media, and (c) excessive dedication to social media, resulting in the individual falling short in other areas, such as social activities, work or school, relationships, and overall well-being (Andreassen and Pallesen 2014).

The literature suggests that coping with stress is a well-documented risk factor for behavioral difficulties, such as excessive smartphone use (Samaha and Hawi 2016), addictive Facebook use (Brailovskaia et al. 2018), internet addiction (Feng et al. 2019), and addiction to social media use (Zhao and Zhou 2021). Additionally, it has been suggested that coping strategies for anxiety, such as time management, along with certain temperament traits, like risk-taking and impulsivity, can sometimes contribute to reckless internet use (Feng et al. 2019; McNicol and Thorsteinsson 2017). Moreover, socioeconomic and cultural factors, such as a lack of sufficient income, educational level, and cultural context, also appear to influence online addiction, suggesting the importance of broader environmental influences to the development of addictive behaviors (Durkee et al. 2012; Hur 2006; Petruzelka et al. 2020; Yang and Tung 2007).

The Compensatory Model is a theoretical model that provides a framework for understanding online addiction (Kardefelt-Winther 2014). This model suggests that individuals engage in activities such as excessive online engagement to cope with challenges such as stress, anxiety, and depression. Excessively engaging in online activities is a strategy that individuals use to escape from the difficulties of everyday stressors and may temporarily relieve negative emotions, such as low self-esteem. However, this strategy often has the opposite results. Reliance on online activities usually creates a vicious cycle in which reliance on online engagement increases feelings of alienation. Over time, online addiction becomes both a symptom and a consequence of underlying psychosocial difficulties (Kardefelt-Winther 2014).

Teenagers and young adults without family responsibilities appear to be more vulnerable to internet addiction as they undergo intense psychosocial changes during their experimental phase of self-discovery (Lozano-Blasco et al. 2022). For instance, a study involving individuals aged 16 to 74 found significant differences in internet addiction, with young men aged 16 to 29 showing higher susceptibility (Bakken et al. 2009). Similarly, another study reported that men aged 18–34 without family obligations (i.e., unmarried) were more susceptible to smartphone addiction within a sample of individuals aged 18 to 65 (Nahas et al. 2018). Young men without family responsibilities appear to be more susceptible to internet addiction compared to their female counterparts of the same age group (Durkee et al. 2012; Kuss et al. 2014). This gender difference is further supported by empirical data showing gender differences in online addiction patterns. Women are more likely to develop addictive behaviors related to social media and online shopping to fulfill needs for connection, socialization, and identity formation, which enhances their sense of “belonging”. In contrast, men are more susceptible to engaging in online activities such as video games, pornography, and gambling for entertainment, pleasure, and alleviating boredom (Andreassen et al. 2012; Andreassen and Pallesen 2014; Chiu et al. 2013; Durkee et al. 2012; Kuss et al. 2014). Note, however, that some studies in the literature have not found a significant relationship between gender and online addiction, highlighting the complexity of this issue (Attanasi et al. 2021; Beranuy et al. 2009; Chiu et al. 2013). Taken together, while family responsibilities and gender are predictors of internet addiction, the interplay of various individual and contextual factors necessitates a comprehensive understanding of this phenomenon.

The literature examining mental health challenges, such as anxiety, worry, and depression, suggests that individuals with problematic internet use tend to experience emotional fluctuations. For instance, studies have shown a link between online addiction and symptoms of depression and anxiety (Anand et al. 2018a, 2018b, 2018c, 2022; Chen et al. 2020; Kennedy et al. 1991; Khubchandani et al. 2021; Kuss et al. 2014; Shadzi et al. 2020; Vishwakarma and Sharma 2021). Specifically, research highlights a bidirectional relationship between depressive symptoms and internet addiction, suggesting that depressive symptoms may both result from and contribute to excessive internet use (Anand et al. 2022; Lim et al. 2017; Vishwakarma and Sharma 2021).

Positive self-esteem acts as a protective mechanism towards mental health, helping individuals manage emotional fluctuations and guarding against excessive reliance on online platforms. Self-esteem refers to an individual’s self-assessment, including how they perceive their character and evaluate their abilities and traits (Santrock 2016). Experiences of success or failure lead to temporary emotional states that influence an individual’s self-esteem level (Brown et al. 2001). Furthermore, self-confidence, which reflects an individual’s beliefs about themselves, shapes their self-definition in both the present and the future (Burns 1982). It acts as an internal guidance mechanism that relates to self-perception and self-awareness concerning their self-esteem (Mann et al. 2004). Together, these elements and an individual’s ability to face life’s challenges and exert control over their circumstances constitute the broader concept of self-esteem (Harter and Leahy 1999). Individuals with low self-esteem often struggle with communication skills, leading them to rely on the internet as a means of escape, which may result in subsequent addiction (Bahrainian et al. 2014). Engagement with the internet has been argued to fill specific gaps, such as low self-esteem (Griffiths 2000a; Kim and Davis 2009; Niemz et al. 2005). For some individuals, the internet provides an opportunity to project idealized versions of themselves, fulfilling gaps in their offline identities and creating alternative social identities (Bahrainian et al. 2014). However, projecting a false image may increase negative emotional states over time, leading to feelings of loneliness and melancholy, further lowering self-esteem (Bahrainian et al. 2014).

The year 2020 is considered the year of extended lockdowns, social distancing, quarantine, and feelings of alienation due to the COVID-19 pandemic. This global crisis disrupted social systems, leading to economic instability and drastically changing daily life and socialization (Ramsetty and Adams 2020; The British Academy 2021). With the shift in work, education, and training to online platforms, social connections also became digital to maintain relationships (Budd et al. 2020). These intense social changes negatively impacted individuals’ mental health and self-confidence, inevitably contributing to a rise in internet and social media addictions (Dong et al. 2020; Feng et al. 2019; Li et al. 2021a; Li et al. 2021b; Zhao and Zhou 2021). Researchers have linked the pandemic to online addiction and its effects on mental well-being (Fernandes et al. 2020, 2021). Although face-to-face interactions decreased significantly during the lockdowns, internet usage significantly increased, resulting in feelings of loneliness, melancholy, and anxiety (Dong et al. 2020; Li et al. 2021b). The pandemic caused a destabilization of prevailing norms, while the internet provided a “safe” and sterilized outlet for entertainment and socialization. On the one hand, online interactions alleviated the anxiety of contracting the virus, and on the other, they exacerbated the feelings of anxiety and melancholic mood associated with internet addiction (Dong et al. 2020; Elhai et al. 2020; Masaeli and Farhadi 2021). In this context, the internet served as a coping mechanism for managing emotions such as anxiety and melancholy in the face of an uncertain future (Bao et al. 2020).

Building on previous studies in the literature that consistently highlight the negative impact of internet and social media addictions, this research investigates these factors in the population of Cyprus after the COVID-19 pandemic, examining their influence on mental well-being and self-esteem. This phase is essential as it enables a deeper understanding of the enduring effects of extended internet usage.

Internet access in Cyprus has grown significantly in recent years, aligning with broader European trends. According to the EU Digital Economy and Society Index (European Commission 2021), household internet subscriptions increased by 15–20 percent between 2016 and 2021, reflecting the rapid integration of digital technologies into daily life (European Commission 2021). Today, Cyprus boasts one of Europe’s highest broadband adoption rates, with fiber-optic technologies enhancing digital connectivity to meet the rising demands for high-speed internet (European Commission 2021). Despite these advancements, digital literacy gaps persist, with 6% of Cypriots possessing digital skills limited to basic online communication, such as searching for information or using messaging platforms, leaving them vulnerable to online risks like fraud and data breaches (European Commission 2021).

Existing research in Cyprus that examined the impact of COVID-19 on individuals’ mental health consistently shows that restrictions of “freedom”, including social distancing, isolation, and other measures that were employed to restrain the spread of the virus, had a profound effect on people’s mental well-being, with anxiety, depression, and stress, amongst others, being profound (Hadjicharalambous et al. 2020; Kleanthous et al. 2023; Mousoulidou et al. 2021; Papageorgiou et al. 2021; Papatriantafyllou et al. 2024). In line with this, data from the Institute for Health Metrics and Evaluation (European Observatory on Health Systems and Policies 2023; IHME n.d.) show that one in six individuals in Cyprus (17.2%) experienced a mental health issue in 2019, which is slightly above the EU average of 16.7%. The most common mental health issues were anxiety disorders (7.2%) and depressive disorders (3.8%), showing the importance of studying mental health in Cyprus. Moreover, a study among female college students in Cyprus (Prodromou et al. 2021) found that 68.5% exhibited varying levels of internet addiction, which correlated positively with psychopathological symptoms such as depression, anxiety, and obsessive–compulsive tendencies. This study highlighted the pervasive impact of internet addiction on mental well-being and the importance of regulating internet use to mitigate its negative effects.

The rising smartphone ownership rates in Cyprus further emphasize this concern. In 2024, smartphone revenue reached USD 95.3 million, with an annual growth rate projected at 2.22% until 2029 (Statista 2023a; Statista n.d.). Cyprus’s smartphone adoption exceeds the EU average, highlighting that it is a tech-savvy population, but it also signals potential risks for problematic use and addiction (CyprusMail 2021). Given these trends, there is an urgent need for further investigation into internet and social media addictions, particularly regarding their long-term impacts on mental well-being and self-esteem.

While previous studies in Cyprus have documented a significant increase in social media and internet use during the pandemic (Papatriantafyllou et al. 2024), to our knowledge, there is no prior research exploring internet and social media addictions after the pandemic. Note, however, that limited research in Greece may offer valuable insights into understanding similar trends in Cyprus. For instance, a study conducted in Greece suggested that the pandemic altered individuals’ digital behavior, with increased use of social media and the internet during that period (Tsourgiannis et al. 2023). Another study focusing on adolescents during the pandemic reported above-average and excessive internet use, with self-esteem being a statistically significant predictor of internet addiction (Touloupis et al. 2023). This significant finding provides a foundation for potential expectations in the current study. For the current research, the following variables were of interest: (a) internet addiction within the population; (b) social media addiction within the population; (c) anxiety, depression, and stress levels; and (d) self-esteem. Based on the existing literature and the purpose of the study, six hypotheses were formulated:

Hypothesis 1 (H1).

The prevalences of internet addiction and social media addiction in the current sample are expected to be high. The existing literature suggests that the pandemic increased digital engagements, and it is anticipated that addiction rates will be elevated in the current sample.

Hypothesis 2 (H2).

There will be associations between demographics (age, gender, and education level) and all variables of interest (internet addiction, social media addiction, mental well-being, and self-esteem). Based on the existing literature, younger individuals are expected to have higher rates of internet and social media addictions. The relationships involving gender and education are less clear due to inconsistent findings in the literature and are therefore examined in an exploratory manner.

Hypothesis 3 (H3).

There will be a positive association between internet addiction and mental well-being. Based on the previous literature, it is expected that individuals with a higher level of internet addiction will have higher levels of depression, anxiety, and stress.

Hypothesis 4 (H4).

There will be a positive association between social media addiction and mental well-being. The previous literature suggests that individuals with a higher score for social media addiction will have higher levels of depression, anxiety, and stress.

Hypothesis 5 (H5).

There will be a negative association between internet addiction and self-esteem. Based on the previous literature, it is expected that individuals with a higher level of internet addiction will have a lower score for self-esteem.

Hypothesis 6 (H6).

There will be a negative association between social media addiction and self-esteem. Previous studies suggest that individuals with a higher score for social media addiction have a lower score for self-esteem.

2. Materials and Methods

2.1. Procedure

Upon the necessary approval from the Cyprus National Bioethics Committee (ΕΕΒΚ ΕΠ 2022.01.105), the measures used in this study were compiled into a single questionnaire created via Google Forms and Survey Monkey. Data were collected via convenience and snowball sampling methods between April and June 2022 to recruit as many participants as possible. The authors distributed invitation calls to participate in the study via social media (e.g., Facebook) and social chatting apps (e.g., Viber, WhatsApp, and Messenger) and emailed them to personal contacts. Prior to completing the online questionnaire, participants were informed about the study’s purpose and duration and assured of their anonymity when participating. Contact information was provided for inquiries or further information on the study. Informed consent for participation was obtained by selecting the “consent” option before completing the questionnaire, which took approximately 10 min. The survey remained online until the required sample of 500 participants was secured.

2.2. Sample

The sample comprised 502 participants, including 372 females (74.1%) and 130 males (25.9%). The ages of individuals in the sample ranged from 18 to 76 years, with 112 of the participants (22.3%) being emerging adults (18–28 years old), 194 of the participants (38.6%) being early-aged adults (29–37 years old), 149 of the participants (29.7%) being middle-aged adults, and 47 of the participants (9.4%) being late-aged adults (47–76 years old). Concerning education level, 14 participants (2.8%) completed secondary education, 160 participants (31.9%) completed a bachelor’s degree, 307 participants (61.1%) completed a master’s degree, and 21 participants (4.2%) completed a PhD. Most of the respondents were married (n = 258, 51.4%), followed by individuals who were single (n = 219, 43.6%) and divorced (n = 25, 5%).

2.3. Measures

Demographic Information: Data were collected on participants’ age, gender, education level, and relationship status.

Internet Addiction: To assess problematic internet use, the Greek version of the Problematic Internet Use Questionnaire (PIUQ-9) (Laconi et al. 2019) was administered. The questionnaire consists of nine questions, each answered on a 5-point Likert-type scale ranging from 1 (never) to 5 (always/almost always). Total scores can range from 9 to 45, with higher scores indicating a greater risk of internet addiction. Specifically, the questionnaire divides participants’ responses into four categories, with (a) no addiction corresponding to a score between 9 and 24, (b) mild indications of problematic use reflecting a score between 25 and 31, (c) borderline problematic use/moderate addiction corresponding to a score between 32 and 38, and (d) problematic use/severe addiction reflecting a score between 39 and 45. Additionally, the total score of the PIUQ-9 is used to assess two factors related to internet addiction: (a) obsession or neglect reflecting a preoccupation with internet use, leading to neglecting daily responsibilities, and (b) impaired control reflecting difficulties in controlling time spent online and feelings of distress when internet access is unavailable. The PIUQ-9 for the current sample indicated good internal consistency with a Cronbach’s alpha value of 0.805.

Social Media Addiction: To measure social media addiction, the Bergen Social Media Addiction Scale (BSMAS) (Andreassen et al. 2016; Dadiotis et al. 2021) [the Greek version] was employed. The BSMAS is a six-item questionnaire designed to assess social media addiction. Respondents rate each item on a 5-point Likert-type scale ranging from 1 (very rarely) to 5 (very often). The total score on the scale ranges from 6 to 30, with higher scores indicating a greater risk of problematic social media use. Specifically, a total score between 6 and 16 suggests no addiction, a total score between 17 and 21 indicates a mild addiction, a total score between 22 and 26 suggests a moderate addiction, and a total score between 27 and 30 indicates severe addiction. The scale evaluates key dimensions of social media addiction, including obsession, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. The BSMAS for the current sample indicated good internal consistency with a Cronbach’s alpha value of 0.812.

Mental Well-Being: To examine mental well-being, the Depression, Anxiety, and Stress Scale-21 (DASS-21) (Lovibond et al. 1995; Pezirkianidis et al. 2018) [the Greek version] was administered. The DASS-21 is a shortened version of the original 42-item DASS-42, developed by the same authors, and it assesses symptoms across three domains: depression, anxiety, and stress. The DASS-21 consists of 21 questions, each rated on a 4-point Likert-type scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). The scale is divided into three 7-item subscales: (a) the Depression Subscale, which evaluates symptoms of depression; (b) the Anxiety Subscale, which assesses symptoms of anxiety; and (c) the Stress Subscale, which measures stress levels. For each subscale, scores are first summed and then doubled to be comparable to the DASS-42 scoring system. The resulting sum score for each subscale allows participants to be classified into five severity categories: (a) normal, (b) mild, (c) moderate, (d) severe, and (e) extremely severe. Higher scores on each subscale indicate greater severity of symptoms in the respective domains. The consistency reliability of the DASS-21 for the current sample was excellent for the depression subscale (α = 0.916) and good for the anxiety (α = 0.852) and stress subscales (α = 0.891).

Self-Esteem: The Rosenberg Self-Esteem Scale (RSES) (Bagley and Mallick 2001; Galanou et al. 2014) [the Greek version] was employed to measure participants’ self-esteem. The RSES consists of ten items answered on a 4-point Likert-type scale ranging from 1 (strongly agree) to 4 (strongly disagree). Five items are positively worded, and five are negatively worded. The scale measures self-esteem by assessing both positive and negative feelings about oneself. Total scores range from 10 to 40, with higher scores showing higher self-esteem. Particularly, total scores ranging from 10 to 24 indicate low self-esteem, total scores ranging from 25 to 35 show moderate self-esteem, and total scores ranging from 36 to 40 indicate high self-esteem. The internal consistency for the current sample was good, with a Cronbach’s alpha value of 0.873.

2.4. Data Processing and Measures

After data collection, several factors were extracted from the questionnaires. The PIUQ-9 was used to extract the total score for each participant, categorizing their internet addiction into four levels: (a) no addiction, (b) mild indications of problematic use, (c) borderline problematic use/moderate addiction, and (d) problematic use/severe addiction. Additionally, the two factors of the PIUQ-9 (obsession or neglect and impaired control) were calculated. Similarly, the BSMAS was used to categorize participants into the four categories of social media addiction: (a) no addiction, (b) mild addiction, (c) moderate addiction, and (d) severe addiction. For the DASS-21, the sum score of the specific items associated with each subscale was computed to obtain the scores of each subscale (depression, anxiety, and stress). Then, following the developer’s instructions, the summed scores for each subscale were adjusted by multiplying the score by two to maintain comparability with the DASS-42. Based on these adjusted scores, participants were classified into five severity categories for each subscale: (a) normal, (b) mild, (c) moderate, (d) severe, and (e) extremely severe. For RSES, total scores were computed by summing participants’ responses across all items. Based on the total scores, participants were categorized into three levels of self-esteem: (a) low, (b) moderate, and (c) high. Additionally, age and education categories were created to facilitate a balanced distribution for analysis. Participants were divided into four age groups: (a) emerging adults (18–28 years old), (b) early-aged adults (29–37 years old), (c) middle-aged adults (38–46 years old), and (d) late-aged adults (47–76 years old). For education, participants were classified into three groups based on their educational level: (a) a low education group, which included participants who completed secondary education, (b) a middle education group, comprising individuals with a bachelor’s degree, and (c) a high education group, which included participants with a master’s and/or doctorate degree.

2.5. Statistical Analysis

All data were entered into the Statistical Package for Social Sciences, Version 29.0 (SPSS 29, IBM Corporation, Armonk, NY, USA); the significance level for all tests was set at 5%; and all the necessary analyses were performed accordingly. Preliminary analyses included calculating descriptive statistics for the various demographic factors as well as determining the prevalence of internet addiction, social media addiction, mental well-being (i.e., depression, anxiety, and stress), and self-esteem in our sample. Univariate ANOVAs and Tukey’s HSD post hoc tests were computed to examine significant differences between demographics (age, education, and gender) and all variables of interest (internet addiction, social media addiction, mental well-being, and self-esteem). Additionally, Chi-Square tests were computed to explore possible associations between categorical demographic variables (i.e., age group and education level) and all variables of interest (i.e., internet addiction, social media addiction, mental well-being, and self-esteem). Pearson correlation analyses were computed to examine relationships between internet addiction and mental well-being and between social media addiction and mental well-being, as well as relationships between internet addiction and self-esteem and between social media addiction and self-esteem. Finally, regression analyses were conducted to assess how well the variables of interest (i.e., demographics, mental well-being, and self-esteem) predicted internet addiction and social media addiction.

3. Results

Hypothesis 1 was not supported. Participants’ levels of internet and social media addictions were generally low. As

Table 1. The distributions of internet addiction and social media addiction in the current sample.

		No Addiction	Mild Addiction	Moderate Addiction	Severe Addiction
Internet Addiction	N	338	143	21	0
	Percent	67.3%	28.5%	4.2%	0%
Social Media Addiction	N	374	112	15	1
	Percent	74.5%	22.3%	3.0%	0.2%

clearly shows, when the participants were divided into the four addiction categories, most participants did not meet the criteria for addiction. However, it is clear from the table that approximately one-third of the participants exhibited mild or moderate levels of addiction, indicating some degree of difficulty with managing internet and social media use.

Hypothesis 2 was partially supported. Age showed significant associations with all the variables of interest, including internet addiction, social media addiction, mental well-being (i.e., depression, anxiety, and stress), and self-esteem. Education showed mixed results, with significant associations found for anxiety and self-esteem but not for internet and social media addictions. Similarly, gender produced mixed findings, with significant differences being observed for social media addiction, anxiety, and stress but no significant associations with internet addiction, depression, or self-esteem.

To examine possible statistically significant differences between age and internet addiction, a univariate ANOVA was computed with internet addiction’s total score as the dependent variable and age groups (i.e., emerging adults, early-aged adults, middle-aged adults, and late-aged adults) as the independent variables. The analysis revealed statistically significant differences between internet addiction and age (F(3,498) = 6.140; p < 0.001). Furthermore, a post hoc analysis showed significant differences in internet addiction levels between late-aged adults and all other age groups, with late-aged adults exhibiting lower scores. Specifically, late-aged adults had significantly lower internet addiction scores compared to emerging adults (MD = −4.068; p < 0.001), early-aged adults (MD = −2.972; p = 0.006), and middle-aged adults (MD = −2.515; p = 0.035). A further examination of internet addiction categories across age groups using a Chi-Square test showed a statistically significant relationship between internet addiction categories and age (${\chi }^2\left(\mathrm{6,502}\right)=14.022;p=0.029$), with notable differences in the distribution of addiction levels among age groups. However, the results are not reported in detail since two cells (16.7%) had expected counts below five, with a minimum expected count of 1.97, indicating a potential violation of the Chi-Square test assumptions.

Furthermore, a univariate ANOVA was computed to examine possible significant differences between age groups and total scores for social media addiction. The analysis showed statistically significant differences between social media addiction and age (F(3,498) = 7.939; p < 0.001). A post hoc analysis using Tukey’s HSD test showed differences between the group of emerging adults and all other age groups, with the emerging adults exhibiting higher levels of social media addiction. Specifically, the emerging adults had higher BSMAS scores than the early-aged adults (MD = 1.376; p = 0.044), the middle-aged adults (MD = 2.361; p < 0.001), and the late-aged adults (MD = 2.956; p < 0.001). A Chi-Square test was considered unreliable because seven cells (43.8%) had expected counts below 5, with a minimum expected count of 0.09, and therefore, the results are not reported here. To examine possible differences in mental well-being across age groups, a univariate ANOVA was computed with age groups as the independent variable and depression, anxiety, and stress scores as the dependent variables. The analysis indicated statistically significant differences between age groups and all measures of mental well-being: depression (F(3,498) = 3.596; p = 0.014), anxiety (F(3,498) = 3.412; p = 0.017), and stress (F(3,498) = 4.572; p = 0.004). Tukey’s HSD post hoc analysis revealed significant differences between emerging adults and middle-aged adults, with the emerging adults scoring higher in depression (MD = 3.219; p = 0.013), anxiety (MD = 2.401; p = 0.022), and stress (MD = 3.288; p = 0.009). Additionally, the emerging adults had a statistically higher stress score than the late-aged adults (MD = 4.397; p = 0.013). Chi-Square tests were computed but were considered unreliable because of missing cells, and therefore, the results are not reported here.

Possible differences in self-esteem across age groups were examined using a univariate ANOVA. The analysis showed a statistically significant difference between age groups and self-esteem (F(3,498) = 4.331; p = 0.002). A post hoc analysis using Tukey’s HSD test showed that the emerging adults had significantly lower self-esteem than the middle-aged adults (MD = −2.198; p = 0.002). A Chi-Square test was conducted to further examine self-esteem across age groups and showed a statistically significant relationship between the self-esteem categories and age (${\chi }^2\left(\mathrm{6,502}\right)=16.004;p=0.014$), with notable differences in self-esteem levels among age groups. Specifically, in the emerging adults, 12.5% of the participants had low self-esteem, 68.8% had moderate self-esteem, and 18.8% had high self-esteem. For the early-aged adults, 8.2% had low self-esteem, 63.9% had moderate self-esteem, and 27.8% had high self-esteem. In the middle-aged adults, 3.4% had low self-esteem, 63.8% had moderate self-esteem, and 32.9% had high self-esteem. Lastly, for the late-aged adults, 2.1% had low self-esteem, 76.6% had moderate self-esteem, and 21.3% had high self-esteem. It is clear from the results that groups of older adults have a higher proportion of individuals with moderate and high self-esteem than groups of younger adults. However, these findings should be interpreted with caution since one cell (8.3%) had expected counts below 5, with a minimum expected count of 3.37.

To examine education, univariate ANOVA tests were conducted to examine possible differences between education and all measures of interest (i.e., internet addiction, social media addiction, mental well-being, and self-esteem). The ANOVA that examined differences between education and internet addiction was not statistically significant (F(2,499) = 2.282; p = 0.103). Similarly, the ANOVA that examined differences between social media addiction and education was also not statistically significant (F(2,499) = 2.282; p = 0.103). Regarding possible differences between mental well-being and education, only the ANOVA that examined anxiety was statistically significant (F(2,499) = 3.039; p = 0.049); the analyses for depression (F(2,499) = 1.075; p = 0.342) and stress (F(2,499) = 0.762; p = 0.467) were not statistically significant. A post hoc analysis using Tukey’s HSD test indicated that the middle education group had significantly higher anxiety than the higher education group. Additionally, the ANOVA that examined differences in self-esteem and education indicated statistically significant differences (F(2,499) = 2.282; p = 0.103). Tukey’s HSD post hoc analysis revealed statistically significant differences between the middle and higher education groups, with the middle education group exhibiting lower self-esteem (MD = −1.392; p = 0.010). A Chi-Square test was computed to explore possible differences between education groups and self-esteem, showing no statistically significant differences (${\chi }^2=6.543;p=0.162$). However, these results should be interpreted cautiously as two cells (22.2%) had expected counts below 5, with a minimum expected count of 1.00. Note that Chi-Square tests were also conducted to examine potential differences in education categories and all the other variables of interest (i.e., internet addiction, social media addiction, and mental well-being), but these were considered unreliable due to potential assumption violations.

Lastly, univariate ANOVA tests were computed to examine possible differences between gender and all measures of interest (i.e., internet addiction, social media addiction, mental well-being, and self-esteem). The ANOVA conducted to examine differences between gender and internet addiction was not statistically significant (F(1,500) = 0.794; p = 0.373). A Chi-Square test was conducted to explore possible differences between gender and level of internet addiction, showing no statistically significant differences (${\chi }^2\left(\mathrm{2,502}\right)=3.280;p=0.194$). The ANOVA examining gender differences in social media addiction was statistically significant (F(1,500) = 8.374; p = 0.004). The Chi-Square test was considered unreliable due to assumption violations. To examine gender differences in mental well-being, separate ANOVAs were conducted for depression, anxiety, and stress. The ANOVA for depression was not statistically significant (F(1,500) = 2.299; p = 0.130). A Chi-Square test was also conducted but was not statistically significant (${\chi }^2=2.593;p=0.628$) and should be viewed cautiously as one cell (10%) had expected counts below 5, with a minimum expected count of 4.14. The ANOVA examining gender differences in anxiety was statistically significant (F(1,500) = 6.584; p = 0.011), and a Chi-Square test showed statistically significant differences between gender and anxiety categories (${\chi }^2\left(\mathrm{4,502}\right)=10.567;p=0.032$). Specifically, 72.8% of females had normal anxiety levels, 5.9% had mild, 10.5% had moderate, 3.0% had severe, and 7.8% had extremely severe anxiety levels. Among males, 84.6% had normal anxiety levels, 1.5% had mild, 8.5% had moderate, 3.1% had severe, and 2.3% had extremely severe anxiety levels. However, these findings should be interpreted with caution since one cell (10%) had expected counts below 5, with a minimum expected count of 3.88. The ANOVA conducted to examine gender differences in stress was also statistically significant (F(1,500) = 7.050; p = 0.008). The corresponding Chi-Square test for stress levels by gender was not statistically significant (${\chi }^2\left(\mathrm{4,502}\right)=6.748;p=0.150$) and should be interpreted cautiously as two cells (20%) had expected counts below 5, with a minimum expected count of 2.59. Finally, the ANOVA examining gender differences in self-esteem was statistically significant (F(1,500) = 5.488; p = 0.020). However, the Chi-square for self-esteem categories by gender was not statistically significant $({\chi }^2\left(\mathrm{2,502}\right)=3.812;p=0.149$).

Hypothesis 3 was supported. To examine the possible positive association between internet addiction and mental well-being, we first explored the mental well-being of our sample. It is clear from

Table 2. The mental well-being of the current sample.

		Normal	Mild	Moderate	Severe	Extremely Severe
Depression (DASS-22)	N	354	54	57	16	21
	Percent	70.5%	10.8%	11.3%	3.2%	4.2%
Anxiety (DASS-22)	N	381	24	50	15	32
	Percent	75.9%	4.8%	9.9%	3.0%	6.4%
Stress (DASS-22)	N	393	43	37	19	10
	Percent	78.3%	8.5%	7.4%	3.8%	2.0%

that most of our sample had normal levels of depression, anxiety, and stress. However, it is worth noting that 7.4%, 9.4%, and 5.8% of participants exhibited severe or extremely severe depression, anxiety, and stress, respectively.

Hypothesis 4 was supported. Pearson r correlations were conducted to examine the relationship between the total scores for internet addiction and scores for mental well-being (i.e., depression, anxiety, and stress). As shown in

Table 3. Correlations between internet addiction and mental well-being.

	Depression	Anxiety	Stress
Internet Addiction	0.299 **	0.235 **	0.319 **
Depression		0.705 **	0.794 **
Anxiety			0.781 **

Note. ** p < 0.01.

, internet addiction was positively correlated with all three measures of mental well-being.

Similarly, to test the possible positive relationship between social media addiction and mental well-being (i.e., depression, anxiety, and stress), a Pearson r correlation was computed. It is clear from

Table 4. Correlations between social media addiction and mental well-being.

	Depression	Anxiety	Stress
Social Media Addiction	0.407 **	0.380 **	0.429 **
Depression		0.705 **	0.794 **
Anxiety			0.781 **

Note. ** p < 0.01.

that social media addiction was positively associated with all three measures of mental well-being, further supporting Hypothesis 4.

Hypothesis 5 was supported. To examine the possible negative association between internet addiction and self-esteem, we first explored our sample’s self-esteem levels. It is clear from

Table 5. Self-esteem levels in the current sample.

		Low	Moderate	High
Self-Esteem (RSES)	N	36	332	134
	Percent	7.2%	66.1%	26.7%

that most participants had moderate to high levels of self-esteem, with only 7.2% reporting low self-esteem.

Pearson r correlations were then conducted to test the relationship between the total scores for internet addiction and self-esteem. The analysis revealed a statistically significant negative correlation between internet addiction and self-esteem ($r=-0.278;p<0.001)$. Higher levels of internet addiction were associated with lower levels of self-esteem, further supporting Hypothesis 5.

Hypothesis 6 was supported. To investigate the potential negative association between social media addiction and self-esteem, a Pearson r correlation was computed. The test indicated a statistically significant negative correlation between social media addiction and self-esteem $(r=-0.351;p<0.001)$. This finding suggests that the higher the social media addiction scores, the lower the levels of self-esteem.

Furthermore, multiple regression analyses were conducted to examine the relationship between internet addiction and all variables of interest (age, education, gender, depression, anxiety, stress, and self-esteem) and between social media addiction and the variables of interest. The first regression, for internet addiction, showed that the model was statistically significant (F(7,494) = 13.045; p < 0.001). The analysis showed that the model explains approximately 15.6% of the variance of internet addiction (R² = 0.156; adjusted R² = 0.144).

Table 6. Multiple regression for internet addiction.

Independent Predictor Variables	B	SE	B	t	Sig.
Age	−0.077	0.027	−0.120	−2.843	0.005
Education	1.275	0.394	0.136	3.237	0.001
Gender	0.237	0.540	0.018	0.439	0.661
Depression	0.026	0.050	0.039	0.511	0.610
Anxiety	−0.048	0.057	−0.057	−0.831	0.406
Stress	0.159	0.053	0.238	2.996	0.003
Self-esteem	−0.198	0.059	−0.174	−3.338	<0.001

shows the findings for internet addiction.

It is clear from Table 6 that age was a significant negative predictor, indicating that older individuals tend to have lower internet addiction scores when all other variables are constant. Education was a significant positive predictor, suggesting that higher levels of education are associated with higher internet addiction scores when all other factors are controlled. Moreover, stress was a significant positive predictor, suggesting that higher levels of stress are associated with higher internet addiction scores when all other variables are constant. Lastly, self-esteem was a significant negative predictor, indicating that individuals with higher self-esteem tend to have lower internet addiction scores when all other factors are controlled. Gender, depression, and anxiety were not found to be significant predictors.

We then computed the second multiple regression analysis with social media addiction as the dependent variable and age, education, gender, depression, anxiety, stress, and self-esteem as the independent variables. The model was statistically significant (F(7,494) = 21.729; p < 0.001) for the variance of internet addiction. The analysis showed that the model explains approximately 23.5% of the variance of social media addiction (R² = 0.235; adjusted R² = 0.225).

Table 7. Multiple regression for social media addiction.

Independent Predictor Variables	B	SE	B	t	Sig.
Age	−0.064	0.021	−0.124	−3.096	0.002
Education	0.177	0.300	0.024	0.589	0.556
Gender	−0.619	0.411	−0.060	−1.507	0.132
Depression	0.040	0.038	0.075	1.047	0.296
Anxiety	0.046	0.044	0.068	1.044	0.297
Stress	0.114	0.041	0.214	2.822	0.005
Self-esteem	−0.140	0.045	−0.154	−3.097	0.002

shows the findings for internet addiction.

As shown in Table 7, age was a significant negative predictor, indicating that older individuals tend to have lower social media addiction scores when all other variables are constant. Stress was a significant positive predictor, suggesting that higher levels of stress are associated with higher social media addiction scores when all other variables are constant. Self-esteem was also a significant negative predictor, indicating that individuals with higher self-esteem tend to have lower social media addiction scores when all other factors are controlled. Education, gender, depression, and anxiety were not found to be significant predictors in this model.

4. Discussion

The current study aimed to examine the prevalence of internet and social media addictions among adults in Cyprus after the COVID-19 pandemic and its relationship with demographics, mental well-being, and self-esteem. Given the significant increase in social media and internet use during the COVID-19 pandemic (Papatriantafyllou et al. 2024), this period was considered essential for exploring potential enduring effects. Overall, the current findings suggest that, while the overall prevalences of internet and social media addictions were low, approximately one-third of the participants exhibited mild to moderate levels of addiction. Younger adults were found to be more prone to internet and social media addictions, and education appeared to play a role in a person’s development of internet addiction. Additionally, internet and social media addictions were positively associated with depression, anxiety, and stress, reflecting the well-documented patterns in the literature regarding the negative impact of excessive online behaviors on individuals’ mental well-being. Lastly, supporting the existing literature (Touloupis et al. 2023), this study shows that self-esteem can act as a protective factor. Individuals with higher scores on internet and social media addictions are more likely to report lower self-esteem. The current study’s findings contribute to the growing body of literature highlighting the adverse effects of internet and social media addictions on mental well-being and self-esteem, offering valuable insights into these relationships in the post-pandemic context.

First, our results show that most of the participants in the current study (approximately 70%) were not classified as being addicted to either internet or social media use. This finding does not support the first hypothesis nor the results of a study conducted in Greece during the pandemic (Touloupis et al. 2023), where higher prevalence rates of internet addiction were observed among adolescents. A possible explanation for this discrepancy is that individuals in our study may have returned to more balanced digital habits after the pandemic. Another explanation is that the difference in age distribution between the studies influenced the overall prevalence rates. Our sample included older individuals who tend to exhibit lower rates of internet addiction, whereas the participants in the study conducted in Greece were adolescents. Although internet and social media addictions were not elevated in the current sample, the difficulties observed in approximately one-third of the participants who exhibited mild to moderate addiction highlight the importance of addressing these tendencies and finding ways to combat them. Interventions promoting healthy online habits and ongoing monitoring are essential, particularly among younger age groups, who appear to be more susceptible to such problematic behaviors.

Second, the current findings align with the literature that consistently shows that demographic factors, especially age and education, are significantly associated with internet and social media use (Durkee et al. 2012; Hur 2006; Petruzelka et al. 2020; Yang and Tung 2007; Lozano-Blasco et al. 2022; Bakken et al. 2009; Touloupis et al. 2023). Our results suggest that younger individuals, particularly emerging adults (i.e., individuals aged 18–28 years old), are more prone to both internet and social media addiction compared to older age groups. This highlights the notion that younger people are more likely to engage in excessive digital behaviors and are more vulnerable to problematic online behaviors. A possible explanation for this finding is that technology is more ingrained into the daily lives of younger adults. This is supported by the finding that older adults, especially those older than 47, had the lowest levels of problematic use. Older individuals’ digital habits appear different and may not rely on technology as much as younger adults. Importantly, our findings show that age is a predictor for both internet and social media addiction, showing the importance of age to the development of this phenomenon. Thus, it is essential for interventions to specifically target younger populations who appear to be involved in more excessive internet and social media use. The relationship between education and internet addiction was not clear. While the ANOVA results show no statistically significant differences in internet addiction across education levels, the regression analysis showed that education was a significant positive predictor of internet addiction. This finding suggests that individuals with higher education levels are more likely to report problematic internet use if other factors are controlled. These mixed findings highlight the complexity of internet addiction, where the interplay of various individual and contextual factors interact to influence this phenomenon. The results of social media addiction are more straightforward. Neither the ANOVA nor the regression results show statistically significant relationships between education levels and social media addiction, suggesting that education does not substantially influence social media behaviors. Lastly, the current study did not find gender differences in internet or social media addictions, aligning with some of the existing literature (Attanasi et al. 2021; Beranuy et al. 2009; Chiu et al. 2013). Further research is recommended for a clearer understanding of this relationship.

The current results contribute to the growing body of research, suggesting a significant relationship between problematic internet and social media use and emotional vulnerability (Anand et al. 2018a, 2018b, 2018c, 2022; Chen et al. 2020; Kennedy et al. 1991; Khubchandani et al. 2021; Kuss et al. 2014; Lim et al. 2017; Shadzi et al. 2020; Vishwakarma and Sharma 2021). Our findings suggest that internet and social media addictions were positively associated with depression, anxiety, and stress. In other words, individuals who scored higher on internet or social media addictions tended to report greater levels of psychological distress. This suggests that excessive internet or social media use may exacerbate feelings of isolation, nervousness, or irritability. Notably, stress appears to play a more direct role in predicting problematic internet and social media use, as the regression results show that when other variables were controlled, stress was a significant positive predictor of both internet and social media addiction. A possible explanation for this is that individuals may use the internet and social media platforms as a means of escaping or coping with stressors, making them more vulnerable to potential addiction. It is also worth noting that within the sample, a subset of participants experienced significant psychological distress. Our results show that while most participants reported normal levels of well-being, between 7 and 9% of our sample reported severe or extremely severe levels of depression, anxiety, and stress. These findings highlight the importance of finding ways to combat these mental health challenges. Researchers have suggested that online addiction may function as a compensatory mechanism, where individuals use online engagement as a way to escape emotional management and cope with anxiety or depression (Kardefelt-Winther 2014). Interventions targeting depression, anxiety, and stress reduction could be particularly beneficial for reducing the psychological distress associated with problematic internet and social media use. Especially useful will be combining these interventions with efforts promoting healthy digital behaviors.

Finally, the current results show a consistent negative relationship between self-esteem and both internet and social media addictions. Individuals with higher internet and social media addiction scores tended to report lower levels of self-esteem. The regression analyses indicated that when controlling for all other variables, self-esteem was a significant negative predictor for both types of addiction, suggesting that individuals with higher self-esteem are less likely to report problematic internet or social media behaviors. This finding supports the existing literature that suggests that self-esteem is a protective factor for mental health, acting as a shield against negative influences (Santrock 2016). Conversely, individuals with low self-esteem may be more vulnerable to excessive internet and social media use, potentially entering a vicious cycle where problematic use further lowers their self-worth. This aligns with the Compensatory Model (Kardefelt-Winther 2014), which suggests that individuals with low self-esteem may engage in activities such as excessive internet and social media use to escape emotional difficulties or compensate for perceived inadequacies in their offline lives. It also supports prior findings that suggest that individuals with low self-esteem may experience difficulties in communication, leading them to use the internet as a means of escape, which may develop into problematic behavior (Bahrainian et al. 2014). Interventions aiming at enhancing self-esteem will be particularly beneficial in reducing the likelihood of problematic online behaviors, especially those that help individuals build a positive self-concept and promote overall psychological well-being.

The current study provides valuable insights into internet and social media addictions and their relationship with mental well-being and self-esteem in the post-pandemic context; however, it is not without limitations. The generalizability of the current findings should be approached with caution due to the use of convenience sampling, which may not fully represent the broader population of Cyprus. In particular, the sample may underrepresent key demographic subgroups, such as individuals from rural areas, older age groups, or varying socioeconomic backgrounds, limiting the overall sample representation. Additionally, this study’s cross-sectional design limits the ability to infer causality between variables, leaving the directionality of relationships, such as that between self-esteem and internet addiction, open to interpretation. Furthermore, the sample consisted mainly of female participants, potentially introducing bias and possibly contributing to the lack of observed gender differences in addiction behaviors. It appears that females were more willing to take part in this study. This gender imbalance may obscure meaningful findings related to male participants or other gender-based variations in internet and social media use. Moreover, while the study examined key variables like depression, anxiety, stress, and self-esteem, other relevant factors, like loneliness or social support, which might drive individuals towards problematic internet and social media use, were not examined. Lastly, the reliance on self-report measures introduces the potential for response bias, which should be considered when interpreting the findings. Future research should aim to address these limitations by employing more diverse sampling methods, obtaining a more balanced gender distribution, and incorporating additional variables to provide a more comprehensive understanding of the phenomenon. Further research should also explore other relevant factors, such as loneliness and social support, to provide deeper insights into the complexities of internet and social media addictions.

5. Conclusions

Internet and social media addictions remain critical concerns in today’s increasingly digitalized world, particularly in the aftermath of the COVID-19 pandemic, when digital engagement reached high levels. Even though the findings of the current study indicate that the overall rates of internet and social media addictions in Cyprus are relatively low, the difficulties observed in approximately one-third of the participants with mild to moderate addiction highlight the urgent need for continued attention to this issue. This study adds valuable insights into the relationship between internet and social media addictions and mental vulnerability, demonstrating their association with depression, anxiety, stress, and self-esteem. Our findings highlight that problematic online behaviors are prevalent among the population, particularly among younger adults, emphasizing the need for targeted interventions for these ages. The results also highlight the role of self-esteem as a protective factor against problematic internet and social media use, supporting the findings from the existing body of research on this subject. Stress was identified as a significant predictor of both internet and social media addictions, suggesting potential long-term implications for mental well-being associated with problematic online behaviors. Given the broader mental health challenges in Cyprus, where a considerable portion of the population faces anxiety and depression, this study underscores the need for further research and tailored interventions. Future studies should explore additional variables, such as social support, to provide a more comprehensive understanding of the factors contributing to internet and social media addictions. The findings of this study can be used by professionals in their efforts to address this growing concern. Initiatives promoting healthier online habits, stress management, and interventions aimed at enhancing self-esteem are highly recommended.