Volume 12, Issue 2 (Spring 2024)                   PCP 2024, 12(2): 123-136 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Jangi Sangani N, Razavi M R. Investigating the Relationship Between the Use of Social Media, Educational Decline, and Student Mental Health. PCP 2024; 12 (2) :123-136
URL: http://jpcp.uswr.ac.ir/article-1-923-en.html
1- Department of Educational Sciences, Faculty of Education Sciences and Psychology, Taybad Branch, Islamic Azad University, Taybad, Iran.
2- Department of Educational Sciences, Faculty of Education Sciences and Psychology, Taybad Branch, Islamic Azad University, Taybad, Iran. , drmrrazavi@gmail.com
Full-Text [PDF 729 kb]   (218 Downloads)     |   Abstract (HTML)  (1963 Views)
Full-Text:   (124 Views)
The widespread use of the internet worldwide has facilitated computer-to-computer communication in a way that the benefits of all information and communications technology are summarized in the features of the internet (Gan, 2018; Kong et al., 2021). Virtual social networks (VSNs) are one of the most important and successful internet programs. VSNs are a new generation of features that are currently in the spotlight of internet users (Kwon & Wen, 2010; Alajmi et al., 2016; Ng et al., 2023). Cyberspace in VSN is one of the most important tools to show the effects of globalization (Ranjbar & Abbasi, 2021). Various networks, such as Facebook, Twitter, WhatsApp, Viber, Telegram, etc. are among the VSNs that have grown rapidly in a short period and are becoming more and more popular among people (Lee et al., 2018). According to a study by Duggan et al. (2015), more than 90% of adolescents use VSNs around the clock. VSNs in cyberspace have led to the evolution and intensity of human communication in different parts of the world and through this, have created a qualitative change in the way humans communicate with each other (Hayes et al., 2016; Sharif & Khanekharab, 2017). Thus, by being present in cyberspace, it is possible to use a lot of information in the shortest possible time and to establish spoken, written, and visual communication at a relatively low cost (Petersen & Johnston, 2015). Meanwhile, Telegram, as a network that is more popular than other networks in Iran and especially among students, needs more attention and research (Naeimi, 2017). The use of social networks has become an integral part of teenagers’ lives and has had a direct impact on all aspects of their lives, including their studies and academic performance (Ng et al., 2023). Spending too much time using VSNs has a significant negative effect on students and disrupts their academic performance (Ali & Qazi, 2023). This study has a special place in Iran because of establishing direct communication with students and the pathology of the realities related to virtual space; therefore, it can help planning, decision-making, and policy-making to deal with threats in cyberspace.

Literature review
In today’s society, considering the influence of VSNs among students and the effects that the use of these networks has on social, cultural, psychological, and physical aspects of the individual, and considering the need to ensure students’ social health, this study aims to investigate the relationship between the use of the Telegram VSN and students’ mental health. On the other hand, achieving the educational goals that schools consider as their main goals requires the provision of students’ mental health (Rostami et al., 2023; Tyagi & Meena, 2023). Numerous studies have shown that student’s mental health is related to their academic performance, and students who somehow suffer from mental health problems or lack of proper mental health are often faced with educational decline (Wood & Scott, 2016; Gao et al., 2018; Lee et al., 2018; and Olszewski-Kubilius & Corwith, 2018; Sabzi et al., 2022; Ng et al., 2023). Therefore, it is necessary to provide appropriate conditions to achieve educational goals and ensure the health of people in society at different ages, through the application of appropriate methods to ensure mental health in schools (Tyagi & Meena, 2023; Mohammadkhani et al., 2024). In the present study, mental health has been considered a necessity for having a healthy life in society, especially the young generation, the extent of which can be affected by the use of VSNs such as Telegram, and these changes are examined in the current study.
Although the Telegram VSN has significant potential to create negative effects on students, it can also serve as a useful and positive resource for students in various aspects if used properly (Yedidia et al., 2003; Prada-Nu˜nez et al., 2020). Increasing students’ motivation can lead to success. Their scores and other psychological aspects of individuals are effective and useful (Teclehaimanot & Hickman, 2011; Metshali et al., 2016). Su´arez-Perdomo et al. (2022) found in a study that more addiction to social media leads to greater students’ academic procrastination. However, there was no significant difference between the groups in terms of academic performance. They mentioned the use of VSNs as a useful resource in the process of primary education of students. Researchers, such as Iglesias-Pradas et al. (2021) and Vosoughi Motlagh et al. (2023) have also noted in their study the increase in students’ academic performance through emergency remote teaching. Alaslani and Alandejani’s (2020) findings also showed that the use of VSNs has a significant positive effect on interaction with classmates, interaction with educators, participation, collaborative learning, and student performance.

Telegram and educational decline
Academic failure is a failure in education, the occurrence of early dropout, repetition of basic education, reduction of grades, or rejection of the educational system (Fredericks, Blumenfeld & Paris, 2004; Beyens et al., 2016). Studies have demonstrated that excessive use of VSNs has a significant negative effect on students, leading to a decline in their educational performance (Seder & Oishi, 2009; Madaiah et al., 2015; Cao et al, 2018; Nú-nez-Guzm´an & Cisneros-Ch´avez, 2019; Suleiman & Sani, 2020). Although the use of these networks facilitates communication with friends (Kirschner & Karpinski, 2010; Fori, 2016), it reduces the study time of students and disrupts their education (Thompson et al., 2008; Fredericks, 2015; Azizi et al., 2019; Suleiman & Sani, 2020). In some studies, the educational decline has been interpreted as the concept of negative effects caused by students entering the virtual space and having fun in VSNs and internet games (Kirschner & Karpinski, 2010; Paul et al., 2012; Rostaminezhad & Shokatirad, 2016).

Telegram and mental health
The use of VSNs can affect different cultural, individual, social, and psychological areas of individuals and society (Saha, 2009; Tyagi & Meena, 2023). One of these areas that are changing by being present in VSNs is the mental health of users, especially the young generation (Uddin et al., 2016). Considering that the young generation is among the largest range of audiences and activists of the Telegram network, and in the present era, due to the possibilities of using the internet, it has become acquainted with cyberspace, and this space has cognitive, emotional, cultural and practice that overshadows them, and the result is an impact on all aspects of life, including their mental health (Mackenna & Bargh, 2006; Steers, 2016; Gil., 2019). Mental health has a very broad meaning and includes mental well-being, a sense of self-sufficiency, adequacy, an understanding of intergenerational solidarity, and the ability to recognize potential intellectual and emotional talents in the individual, in such a way that the individuals can recognize one’s abilities, adapt to the usual stresses of life, and be productive and useful professional (Goldsmith, 2000; Pertegal-Vega et al., 2019). Every individual who can cope with their deeper problems, compromises with themselves and others, is not incapable of their inevitable inner conflicts, does not reject himself from society, and has mental health (Morgan & Cotton, 2003; Ng et al., 2023). Tateno et al. (2004) stated a significant and clear relationship between the weekly hours of internet use and mental health scores. Their findings show that the association between internet use and mental disorders, such as anxiety and stress, is increasing. Mental health leads to the continuation of optimal job, social, and educational performance. Evidence also suggests a relationship between social media use and various aspects of adolescent health, including sleep and mental health (Pantic et al., 2012; Woods & Scott, 2016; Ali & Qazi, 2023). The prevalence of poor-quality sleep among adolescents causes mental health disorders and symptoms of mental distress, such as depression, anxiety, and low self-esteem (Pittman & Reich, 2016; Gao et al., 2018).
Accordingly, studies indicate the effect of using VSNs on the mental health of adolescents (Tandoc et al., 2015; Elhai et al., 2016; Ceglarek & Ward, 2016; Rosenthal et al., 2016; Abbasi & Alghamdi, 2017; Razavi, 2021). Christensen (2018) showed that the more time a person spends on social media, the greater the negative impact on their overall emotional health and the lower the quality of their relationships with others (Ali & Qazi, 2023). This issue will negatively affect their relationship in the next stage.
Continued use of VSNs leads to reduced performance in the areas of social responsibility, social support, academic and work performance, and a sense of self-worth (Deimazar et al., 2019). Yang (1998) found that 58% of students who overused the internet experienced a severe educational decline in study habits, significant reductions in grades, and increased absenteeism. Researchers, such as McLaughlin and King (2015), Woods & Scott, (2016), Primack et al. (2017), Olszewski-Kubilius & Corwith (2018), and Lee et al. (2018) stated in their research that the high use of VSNs and recreational use of the internet is associated with serious harms, such as educational decline and reduced mental health in the form of depression and anxiety. According to Ahmed (2013), the most alarming part of VSN use is its negative impact on the student’s academic performance. On the other hand, Navarro et al. (2018) declared that addicted users to VSNs are more exposed to potential mental health issues; therefore, too much use of VSNs, due to reduced study time, has negative impacts on this field, which can adversely affect their educational and mental health outcomes (Yedidia et al., 2003; Seder & Oishi, 2009). Table 1 shows the variables related to this study.

The context of the study
Khaf City is one of the borders and deprived areas located in the northeast of Iran. In this region, as in many other regions of Iran, proper culture and information for the correct use of virtual space have not been done. Accordingly, in 2017, due to the lack of control of students by teachers and parents in the use of mobile phones and the internet, three teenagers in Khaf City, Iran, became victims of the dangerous game of blue whale. Therefore, before using these tools and social networks, the necessary culture-building should be done in society. In the field of education, the Telegram VSN is a research field for comprehensive information on students that is growing rapidly. The limited amount of use of the VSN and their use to communicate with professors, family, and friends can make students achieve positive academic and academic performance. However, the reverse is also true. Therefore, spending too much time using the VSN hurts students and disrupts their academic and academic performance. The studies conducted in this field have only focused on the use of the Facebook social network, and the use of the widely used VSN, such as Telegram, has not been studied. Research has been done regarding the impact of other networks with two different variables in Iran, but the connection of Telegram to the two variables in question has not been investigated. Also, the relationship between the variables was separated in the previous research and this is the first research that examines the variables simultaneously. Telegram is a messaging application that focuses on message transfer with speed and security (Abbasi Shavazi & Homayoon, 2014). This application is simple and free to use. Telegram can send photos, videos, text, and any type of file (Cheung et al., 2011; Marino et al., 2016; Oeldorf-Hirsch & Sundar, 2016). In this regard, it is necessary to have an estimate of the influence of these networks among students and try to provide a solution to minimize the problems caused by the use of these networks. The results of the present study can have beneficial effects on reducing the rate of educational decline and the mental health of students. Also, education and society can benefit from it. However, neglecting the impact of the Telegram VSN on the mental health and educational decline of students primarily affects students and later stages of the education system and even all levels of society. In terms of the innovation of this research, we can mention the simultaneous examination of the relationship between three variables. Besides some literature pointing to the positive effects of these networks (Erfanian et al., 2013; Oxford University Press, 2015), there are still many studies that point to the negative effects (Kuss & Griffiths, 2011; Choi & Lim, 2016; Ahmed, 2013; Navarro et al., 2018). Thus, the main purpose of the study is to examine the correlations between the use of Telegram, educational decline, and mental health.

To achieve the goals of this research, the following hypotheses have been developed:
There is a relationship between the use of Telegram VSN and the rate of educational decline of second-year high school students in Khaf City, Iran;
There is a relationship between the use of Telegram VSN and the level of mental health of second-year high school students in Khaf City, Iran;
There is a difference between the use of Telegram VSN by male and female high school students in Khaf City, Iran.

Study variables
In the present study, VSN was considered as an independent variable, and educational decline and mental health variables were considered as dependent variables.

Materials and Methods

Research Method

The current research was a quantitative survey with a correlational design. Data collection was done using three questionnaires. The inclusion criteria were being a student of Khaf City, Iran, a secondary school student, having access to virtual social networks, and being willing to participate in the study, while the exclusion criteria were the simultaneous use of other virtual networks except Telegram, and incomplete completion of questionnaires.
Statistical population, sample size, and sampling method
In this research, a stratified random sampling method was used. The statistical population of this study, based on the statistics received from the education department of Khaf City, Iran, includes all female and male students in secondary schools in the second year of Khaf City, Iran. The statistical population consisted of 550 girls and 700 boys, a total of 1250 students. The sample size according to the Cochran formula, with an error level of 5% (129 girls and 165 boys) was 294 people (Table 2).

Data collection method
Data collection tools include a researcher-made questionnaire on the use of the Telegram VSN and Keyes (2005) multidimensional model of mental health.

Telegram virtual social network
Since users of the Telegram VSN are also considered internet users, to assess the degree of addiction to the Telegram VSN, a researcher-made questionnaire available on the Telegram VSN was used. This questionnaire has 30 questions and aims to evaluate the positive and negative effects of VSNs from different dimensions. It is scored based on a 5-point Likert scale. This questionnaire includes various items, such as “VSNs on the internet expand the scope of communication and using VSNs” and “You can find friends who are out of reach”, that measure the use of Telegram VSNs. To determine the validity of the tool, validity of the tool was determined by the content validity method and by considering the opinions and suggestions of the supervisor. Then, by applying the corrections and changes requested by professors and experts, the final questionnaire was developed and its validity was confirmed.

Academic performance of students questionnaire
The academic performance of students questionnaire (APSQ) was implemented to access the desired information from the students. This tool has two parts, including demographic data (age, gender, field, academic year, and grade point average in the last two semesters) and 15-item scale questions related to using VSNs. In the design of the questions, a 5-point Likert scale with anchors that range from 1 (totally disagree) to 5 (totally agree) was used. The participants’ grade point average in the last two semesters was used to assess the academic performance (Razavi, 2021) of male and female students, and a structured self-administrated questionnaire (reliability α=0.979) was used in testing the use of VSNs.

Keyes multidimensional model of mental health
Keyes (2005) model of mental health was derived from the long form of the mental health continuum and consists of 14 questions and 3 components. This multidimensional questionnaire is based on a 6-point Likert scale ranging from 1 (strongly agree) to 6 (strongly disagree) and assesses mental health with questions, such as (the feeling that you have a job or something important to offer to the community). This questionnaire has three dimensions, namely emotional well-being, mental health, and social health. In this questionnaire, scale (happiness, interest in life, and satisfaction) to show feelings and emotions (emotional well-being), 6 items (self-acceptance, positive relations with others, personal growth, purpose in life, environmental mastery, and autonomy) were selected to measure mental health and 5 items (community participation and assistance, community cohesion, social prosperity, social acceptance, and social attention and understanding) were selected to demonstrate social health. The minimum and maximum limits of the score are 14 and 84, respectively. A score between 14 and 28 indicates poor mental health; a score between 28 and 56 indicates moderate mental health and a score higher than 56 indicates high mental health. In the present study, the reliability of the questionnaire based on the Cronbach α coefficient was estimated to be 0.82. The validity of the questionnaire was also confirmed by professors and experts.

Research method
First, after coordinating with the directors of the Khaf education department and providing information about the research to principals, teachers, and students of secondary schools, a researcher-made questionnaire on the use of the Telegram VSN and Keyes (2005) model of mental health among students were distributed and to know the rate of educational decline, the current grade point average and their previous year were examined and finally, the data were analyzed.

Data analysis
Descriptive (Mean±SD) and inferential (analysis of variance, Pearson correlation, and paired t-test) statistical methods were used to analyze the collected data. Finally, the data were entered into the SPSS software, version 24) and a one-sample Kolmogorov-Smirnov test was used to check the normality of the data.
Table 3 shows the results of descriptive statistics of the variables (standardized scores) of Telegram VSN use (4.206±1.095 h/day), mental health (3.867±1.063; cumulative scores of the related questionnaire), and educational decline (3.367±1.272; cumulative scores of the related questionnaire). Considering that the obtained mean of Telegram VSN use is in the range (3.66-5), they are at a high level. Also, the obtained mean of educational decline is in the range (2.34-3.66), it is at a medium level.

The negativity of the skewness of both variables indicates the long distribution to the left, on the other hand, the positive skewness means that the shape of the variable is longer than the normal distribution. To test the hypothesis of the normality of the studied variables, the Kolmogorov-Smirnov test was used (Table 4). If the P value in the Kolmogorov-Smirnov test is greater than 0.05, then the data is normal. Table 4 shows the P of Telegram VSN use (0.112), mental health (0.103), and educational decline (0.067) which are more than 0.05. Therefore, it shows the normality of all variables (P>0.05). Accordingly, parametric tests can be used.


Testing research hypotheses

Hypothesis 1: There is a relationship between the use of the Telegram VSN and the level of mental health and educational decline of second-year high school students in Khaf City, Iran.
Table 5 shows the results of the Pearson correlation test between the use of Telegram VSN with the rate of educational decline and students’ mental health.
According to the results of Table 5, because the significance level of both variables (educational decline and mental health) is less than 0.05, there is a relationship between the use of the Telegram VSN and the level of mental health (-0.258) and educational decline (0.309) of second-year high school students in Khaf City, Iran. To evaluate the effect of using the Telegram VSN on the rate of educational decline and students’ mental health, regression model fitting was used.

Linear regression analysis
Table 6 shows the results of regression analysis between the variables (regression adequacy indices) to fit the regression model.
Table 6 provides the R and R2 values. The R-value represents a positive linear relationship between the Telegram VSN and educational decline (R=0.309) and a negative linear relationship between the Telegram VSN and mental health (R=-0.258). Also, the coefficient of determination (R2) for the variables of educational decline and students’ mental health were 9% and 6%, respectively. These values show that 9% and 6% of the changes in the use of Telegram VSN are equal to the rate of educational decline and mental health. In other words, linear regression can describe 9% and 6% of the data variance, respectively. Because these values ​​do not consider the degree of freedom, the adjusted coefficient of determination is used for this purpose, which in this test is equal to 9% and 6%, respectively. Given that the Durbin-Watson statistic value for both variables is in the standard range of 1.5 to 2.5, the independence of the residual (errors) can be concluded. Therefore, among the data, there is no first-order self-correlation. Also, there is a linear relationship between the variables in our model. About the educational decline variable, the values of the R square and the adjusted R square are 0.095 and 0.086. Also, concerning the mental health variable, the values of the R square and the adjusted R square are 0.066 and 0.056. The proximity of these two values (R square and adjusted R square) to each other in both variables (educational decline and mental health) indicates that the variables used in the model can be used well and provide a good fit. In the case of evaluating two models, with almost identical coefficients of determination, a model with a smaller standard error of the estimated value is selected. According to the mentioned indicators, for both variables, the model is sufficient.

In Table 7, the significance of the regression is calculated from the F test. The larger the value of F, the closer the significance level is to zero. A value smaller than 0.05 for the significance level indicates a suitable regression model. The F test shows whether the variability of the scores of two sets is different from the independent scores of the total average. In other words, is there a significant difference between their variances? The larger the value of F, the closer the significance level is to zero. A value smaller than 0.05 for the significance level indicates a suitable regression model. According to the P value of the test in Table 6, the level of significance calculated for these variables of educational decline and mental health is equal to 0.002 and 0.012, respectively. Therefore, the null hypothesis is rejected with a confidence of 0.95%. Accordingly, the existing model is significant with a confidence of 0.95, and based on the available data, this model can predict changes in educational decline and mental health. According to the results of Table 7, with the increase in the use of Telegram VSNs, the academic decline increases, so there is a direct relationship between these two variables. However, with the increase in the use of Telegram VSNs, mental health decreases. Therefore, there is an inverse relationship between these two variables.

Hypothesis 2: There is a significant difference between the use of Telegram VSN by male and female high school students in Khaf City, Iran.
To test this hypothesis, the test comparing the means of two independent populations was used (Table 8).

The P<0.05 (0.004), can be attributed to the difference between the mean use of the Telegram VSN between men and women is significant. Men use the Telegram VSN more.

This correlation study was conducted in high schools in Khaf City, Iran, in the academic year of 2018-2019, and 1250 students (550 girls and 700 boys) were selected by simple stratified random sampling. The mean of Telegram VSN use was at a high level. Also, the mean of educational decline was at a medium level. The results of this study showed a relationship between the use of the Telegram VSN and the rate of educational decline of secondary school students in Khaf City, Iran. The findings of this study are in line with the research of Fredericks et al. (2004), Thompson et al., (2008), Seder & Oishi, (2009), Paul et al. (2012), Fredericks (2015), Madaiah et al. (2015), Cao et al, (2018); Nú-nez-Guzm´an & Cisneros-Ch´avez (2019), Suleiman & Sani, (2020), and Ng et al. (2023). According to all these studies, spending too much time on VSNs reduces study hours and students’ fatigue in the classroom and leads to educational decline.
In explaining this finding, one of the most important reasons for the negative impact of using the Telegram VSN on students’ educational decline is the reduction of study time due to the use of these networks (Ahmed, 2013; Ogundijo, 2014; Fori, 2016; Razavi, 2021). Since communication is the main reason for Telegram VSN users, they can communicate with a large number of people in a limited time and easily, and this can lead to excessive use of these networks. Excessive time spent in these networks reduces the amount of study, and also because most students spend long hours of the night in these networks, they cannot attend classes with sufficient concentration. These factors can lead to educational decline. As a result, spending too much time using the Telegram VSN hurts students’ educational decline.
Another finding of this study showed that there is a relationship between the use of the Telegram VSN and the mental health of second-year high school students in Khaf City, Iran. The findings of this research are similar to Pantic et al. (2012), Uddin et al., (2016), Woods & Scott, (2016), Elhai et al. (2016), Ceglarek & Ward, (2016), Rosenthal et al. (2016), Primack et al. (2017), Abbasi & Alghamdi, (2017), Christensen (2018), Pertegal-Vega et al. (2019), Razavi (2021) and Ng et al. (2023), and Tyagi & Meena, (2023). According to all studies, the use of VSNs affects the mental health of students and thus there is a relationship between the use of VSNs and the level of mental health.
In explaining this finding, excessive use of the Telegram VSNs, due to its addictive nature, as well as social, psychological, and emotional consequences, with the ability to influence and affect student performance. This can affect their physical, mental, and family life and lead to mental health disorders and symptoms of mental distress, such as depression and anxiety as well as low self-esteem (Pittman & Reich, 2016; Gao et al., 2018; Ali & Qazi, 2023; Ng et al., 2023).
The findings of this study also showed that there is a significant difference between the use of Telegram VSN by male and female high school students in Khaf City, Iran. The results of this study showed that the use of Telegram VSNs is higher in men. Boyd and Ellison (2007) stated that the effect of gender on the use of VSNs is significant. Pantic et al. (2012) showed that the use of VSNs is higher among boys than girls. Tyagi & Meena, (2023), declared that the use of VSNs is gender-sensitive.

Due to the increasing importance and impact of using VSNs, such as Telegram on the educational decline and mental health of students, the present study, investigates the relationship between the use of Telegram VSN and the rate of educational decline and mental health of high school students in Khaf. The results of the data analysis showed that there is a relationship between the use of the Telegram VSN and the rate of mental health and the rate of educational decline of secondary school students in Khaf City, Iran. The more a student uses VSNs during the day and the more time he/she spends, the more educational decline they will have and the more they will endanger student mental health. VSNs such as Telegram, WhatsApp, Viber, Line, Facebook, Instagram, etc. are readily available to students. Excessive use of these VSNs leads to internet addiction, on the one hand, it endangers the mental health of students, and on the other hand, it hinders students’ academic progress and causes educational decline.
According to the results of this study, there is a relationship between the use of Telegram VSN with the rate of educational decline and the rate of students’ mental health. This should be included in the programs and measures of education programs. The results of this study play an important role in introducing the community of researchers in the field of education and health, to the effect of using Telegram VSN on the rate of educational decline and mental health of students due to the increasing use of such spaces among students; hence, it is the strength of this study. In this regard, it is necessary to provide conditions for the use of VSNs and the internet to serve the purposes of education, and the use of these networks will lead to academic achievement and improve the mental health of students. Because this research is limited to Telegram, it should be careful in generalizing the results to other VSNs. Because this research was limited to Khaf City, Iran, therefore, caution should be exercised in generalizing the results to other cities. Because this research is limited to the secondary school period, caution should be exercised in generalizing the results to other periods. It is also necessary to provide valuable information on the positive and negative effects of using VSNs, especially Telegram to users of these networks. Also, measures should be taken to introduce and create a useful and scientific use of VSNs in schools. Considering the existence of a relationship between educational decline and mental health, it seems that educational decline plays a mediating role between the use of VSNs and mental health. Therefore, it is suggested to investigate the mediating role of the educational decline in the relationship between the other two variables in future research. Finally, it is suggested to hold briefing sessions for officials on how to use VSNs. Since the high use of virtual networks affects the academic decline and mental health of students, it is necessary to hold separate briefing sessions about the disadvantages of using Telegram for parents and students.

Ethical Considerations

Compliance with ethical guidelines

There were no ethical considerations to be considered in this research.

This research did not receive any grant from funding agencies in the public, commercial, or non-profit sectors.

Authors' contributions
Conceptualization and supervision: Majid Reza Razavi;  Data collection: Naser Jangi Sangani; Investigation, data analysis and writing: All authors.

Conflict of interest
The authors declared no conflict of interest.

The cooperation of all participants in the research is thanked and appreciated.

Abassi Shavazi, M. T., & Homayoon, P. (2014). [Social media and social relationship: A study of relationship between new communication technologies and social isolation (Persian)]. Cultural Studies and Community, 10(36), 43-66. [Link]
Abbasi, I. S., Alghamdi, N. G. (2017). When flirting turns into infidelity: The Facebook dilemma. The American Journal of Family Therapy, 45(1), 1-14. [DOI:10.1080/01926187.2016.1277804]
Ahmed, M. (2013). Survey on the social impacts of online social networking sites. Journal of Computer Engineering, 13(6), 18-20. [DOI:10.9790/0661-1361820]
Alajmi, M. A., Alharbi, A. H., & Ghuloum, H. F. (2016). Predicting the use of Twitter in developing countries: Integrating innovation attributes, uses and gratifications, and trust approaches. Informing Science: The International Journal of an Emerging Transdiscipline, 19, 215-237. [DOI:10.28945/3534]
Alaslani, K., & Alandejani, M. (2020). Identifying factors that influence student’s performance through social networking sites: An exploratory case study. Heliyon, 6(4), e03686.[DOI:10.1016/j.heliyon.2020.e03686] [PMID]
Ali, A., & Qazi, I.A. (2023). Countering misinformation on social media through educational interventions: Evidence from a randomized experiment in Pakistan. Journal of Development Economics, 163, 103108. [DOI:10.1016/j.jdeveco.2023.103108]
Azizi, S. M., Soroush, A., & Khatony, A. (2019). The relationship between social networking addiction and academic performance in Iranian students of medical sciences: A cross-sectional study. BMC Psychology, 7(1), 28. [DOI:10.1186/s40359-019-0305-0] [PMID]
Beyens, I., Frison, E., & Eggermont, S. (2016). “I don’t want to miss a thing”: Adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook-related stress. Computers in Human Behavior, 64, 1-8. [DOI:10.1016/j.chb.2016.05.083]
Boyd, D. M, & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230. [DOI:10.1111/j.1083-6101.2007.00393.x]
Cao, X., Masood, A., Luqman, A., & Ali, A. (2018). Excessive use of mobile social networking sites and poor academic performance: Antecedents and consequences from stressor-strain-outcome perspective. Computers in Human Behavior, 85, 163-174. [DOI:10.1016/j.chb.2018.03.023]
Ceglarek, P. J., & Ward, L. M. (2016). A tool for help or harm? How associations between social networking use, social support, and mental health differ for sexual minority and heterosexual youth. Computers in Human Behavior, 65, 201-209. [DOI:10.1016/j.chb.2016.07.051]
Cheung, M. K, Chiu, P. Y., & Lee, M. K. O. (2011). Online social networks: Why do students use Facebook? Computers in Human Behavior, 27(4), 1337-1343. [DOI:10.1016/j.chb.2010.07.028]
Choi, S. B., & Lim, M. S. (2016). Effects of social and technology overload on psychological well-being in young South Korean adults: The mediatory role of social network service addiction. Computers in Human Behavior, 61, 245-254. [DOI:10.1016/j.chb.2016.03.032]
Christensen, S. P. (2018). Social media use and its impact on relationships and emotions [MA thesis]. Provo: Brigham Young University.
Deimazar, G. H. A. S. E. M., Kahouei, M. E. H. D. I., Forouzan, M., & Skandari, F. (2019). [Effects of online social networks on sleep quality, depression rate, and academic performance of high school students (Persian)]. Koomesh, 21(2), 312-317. [Link]
Duggan, M., Smith, A. (2015). Demographics of key social networking platforms. Washington, Pew Research Center. [Link]
Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2016). Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Computers in Human Behavior, 63, 509-516. [DOI:10.1016/j.chb.2016.05.079]
Erfanian, M., Javadinia, S. A., Abedini, M. R., & Bijari, B. (2013). Iranian students and social networking sites: Prevalence and pattern of usage. Procedia Social and Behavioral Sciences, 83, 44-46. [DOI:10.1016/j.sbspro.2013.06.009.]
Gan, C. (2018). Gratifications for using social media: A comparative analysis of Sina Weibo and WeChat in China. Information Development, 34(2), 139-147. [DOI:10.1177/0266666916679717]
Gao, T., Li, J., Zhang, H., Gao, J., Kong, Y., & Hu, Y., et al. (2018). The influence of alexithymia on mobile phone addiction: The role of depression, anxiety and stress. Journal of Affective Disorders, 225, 761-766. [DOI:10.1016/j.jad.2017.08.020] [PMID]
Gil, J., De Besa, M. R., & Garz’on-Umerenkova, A. (2019). Why do university students procrastinate? An analysis of the reasons and characterization of students with different reasons for procrastination. Revista de Investigaci’on Educativa, 38(1), 183-200. [DOI:10.6018/rie.344781]
Fori, E. (2016). The effects of social networking sites on the academic performance of the engineering student in the University of Maiduguri, Borno State, Nigeria. International Journal of Computer Science Issues, 13(1), 76-84. [DOI:10.20943/IJCSI-201602-7684]
Fredericks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. [DOI:10.3102/00346543074001059]
Fredricks, J. A. (2015). Academic engagement. In: J. D. Wright (Ed), International encyclopedia of the social & behavioral sciences (pp. 31-36). Amsterdam: Elsevier. [DOI:10.1016/B978-0-08-097086-8.26085-6]
Hayes, R. A., Carr, C. T., & Wohn, D. Y. (2016). One-click, many meanings: Interpreting paralinguistic digital affordances in social media. Journal of Broadcasting & Electronic Media, 60(1), 171-187. [DOI:10.1080/08838151.2015.1127248]
Iglesias-Pradas, S., Hernández-García, Á., Chaparro-Peláez, J., & Prieto, J. L. (2021). Emergency remote teaching and students’ academic performance in higher education during the COVID-19 pandemic: A case study. Computers in Human Behavior, 119, 106713. [DOI:10.1016/j.chb.2021.106713] [PMID]
Keyes, C. L. M. (2005). Mental Illness and/or Mental Health? Investigating Axioms of the Complete State Model of Health. Journal of Consulting and Clinical Psychology, 73(3), 539–548. [DOI:10.1037/0022-006X.73.3.539] [PMID]
Kirschner, P.A., & Karpinski, A.C. (2010). Facebook and academic performance. Computers in Human Behavior, 26(6), 1237-1245. [DOI:10.1016/j.chb.2010.03.024]
Kong, Q., Lai-Ku, K. Y., Deng, L., & Yan-Au, A. C. (2021). Motivation and perception of Hong Kong University students about social media news. Comunicar, 29(67). [DOI:10.3916/C67-2021-03]
Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction--a review of the psychological literature. International Journal of Environmental Research and Public Health, 8(9), 3528–3552. [DOI:10.3390/ijerph8093528] [PMID]
Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254-263. [DOI:10.1016/j.chb.2009.04.011]
Lee, Y.K., Chang, C. T., Cheng, Z. H., & Lin, Y. (2018). How social anxiety and reduced self-efficacy induce smartphone addiction in materialistic people. Social Science Computer Review, 36(1), 36-56. [DOI:10.1177/0894439316685540]
McKenna, K. Y. A., & Bargh, J. A. (2000). Plan 9 from Cyberspace. The implication of internet for personality and social psychology. Personality and Social Psychology Review, 4(1), 57-75. [DOI:10.1207/S15327957PSPR0401_6]
Madaiah, M., Seshaiyenger, C., Suresh, P., Munipapanna, S., & Sonnappa, S. (2017). Study to assess the effects of social networking sites on medical college students. International Journal of Community Medicine and Public Health, 3(5), 1204–1208. [DOI:10.18203/2394-6040.ijcmph20161385]
Marino, C., Vieno, A., Pastore, M., Albery, I. P., Frings, D., & Spada, M. M. (2016). Modeling the contribution of personality, social identity and social norms to problematic Facebook use in adolescents. Addictive Behaviors, 63, 51-56. [DOI:10.1016/j.addbeh.2016.07.001] [PMID]
McLaughlin, K. A., & King, K. (2015). Developmental trajectories of anxiety and depression in early adolescence. Journal of Abnormal Child Psychology, 43(2), 311-323. [DOI:10.1007/s10802-014-9898-1] [PMID]
Metshali, M. A. Maistry, S. M., & Govender, D. W. (2015). Online chats: A strategy to enhance learning in large classes. South African Journal of Education, 35(4), 1-9. [DOI:10.15700/saje.v35n4a1215]
Mohammadkhani, S., Akbarian Firoozabadi, M., Akbari, M., & Nasiri, M. (2024). Metacognitions about Health and COVID-19 preventive behaviors: Experiential avoidance and coronavirus anxiety as mediators. Practice in Clinical Psycholoy, 12(1), 33-42. [DOI:10.32598/jpcp.12.1.877.1]
Morgan, C., & Cotten, S. R. (2003). The relationship between internet activities and depressive symptoms in a sample of college freshmen. Cyberpsychology & Behavior, 6(2), 133-142. [PMID]
Navarro, R., Larrañaga, E., & Yubero, S. (2018). Differences between preadolescent victims and non-victims of Cyberbullying in cyber relationship motives and coping strategies for handling problems with peers. Current Psychology, 37(1), 116-127. [Link]
Nú˜nez-Guzm’an, R., & Cisneros-Ch’avez, B. (2019). Adicción a redes sociales y procrastinación académica en estudiantes universitarios. Nuevas Ideas en Informática Educativa, 15(1), 114-120. [Link]
Oeldorf-Hirsch, A., & Sundar, S. S. (2016). Social and technological motivations for online photo sharing. Journal of Broadcasting & Electronic Media, 60(4), 624-642. [DOI:10.1080/08838151.2016.1234478]
Ogundijo, O. (2014). Use of social networking among secondary school students: Implications for academic performance. Scientific Research Journal (SCIRJ), 2(5), 17-34. [Link]
Olszewski-Kubilius, P., & Corwith, S. (2018). Poverty, academic achievement, and giftedness: A literature review. Gifted Child Quarterly, 62(1), 37-55. [DOI:10.1177/0016986217738015]
Oxford University Press (2015). Learn about virtual learning environment/course management system content. Retrived from: [Link]
Pantic, I., Damjanovic, A., Todorovic, J., Topalovic, D., Bojovic-Jovic, D., & Ristic, S., et al. (2012). Association between online social networking and depression in high school students: Behavioral physiology viewpoint. Psychiatria Danubina, 24(1), 90-93. [Link]
Prada-Nu˜nez, R., Hernandez-Su’arez, C. A., & Maldonado-Estevez, E. A. (2020). [Diagn’ostico del potential de las redes sociales como recurso did’actico en el proceso de ense˜nanza en ‘epoca de aislamiento social (Spanish)]. Revista Espacios, 41(42), 260-268. [DOI:10.48082/espacios-a20v41n42p22]
Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Computers in Human Behavior, 28(6), 2117-2127. [DOI:10.1016/j.chb.2012.06.016]
Pertegal-Vega, M. A., Oliva-Delgado, A., & Rodríguez-Meirinhos, A. (2019). Systematic review of the research landscape on social networks: Taxonomy on user experiences. Comunicar, 27(60), 81-91. [DOI:10.3916/C60-2019-08]
Petersen, C., & Johnston, K. A. (2015). The impact Facebook and Twitter has on the cognitive social capital of University Students. Informing Science: The International Journal of an Emerging Transdiscipline, 18, 1-30. [DOI:10.28945/2160]
Pittman, M., & Reich, B. (2016). Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words. Computers in Human Behavior, 62, 155-167. [DOI:10.1016/j.chb.2016.03.084]
Primack, B.A., Shensa, A., Sidani, J.E., Whaite, E.O., Lin, L.Y., & Rosen, D., et al. (2017). Social media use and perceived social isolation among young adults in the U.S. American Journal of Preventive Medicine, 53(1), 1-8. [DOI: 10.1016/j.amepre.2017.01.010] [PMID]
Ranjbar, Q., & Abbasi, M. (2021). Recognizing the effects of cyberspace on international peace and security in the age of globalization based on castells’ theory. International Journal of Political Science, 11(2), 23-38. [Link]
Razavi, M. (2021). Gender differences in the effect of virtual social network use on students’ academic performance. Current Psychology, 40, 744-750. [DOI:10.1007/s12144-018-9991-7]
Rosenthal, S. R., Buka, S. L., Marshall, B. D., Carey, K. B., & Clark, M. A. (2016). Negative experiences on Facebook and depressive symptoms among young adults. The Journal of Adolescent Health: Official Publication of The Society for Adolescent Medicine, 59(5), 510–516. [DOI:10.1016/j.jadohealth.2016.06.023] [PMID]
Rostaminezhad, M. A., & Shokatirad, A. R. (2016). [Predicting students’ membership in virtual networks and their academic performance based on parenting styles and adjustment (Persian)]. Journal of Applied Psychology, 10(3),193-208. [Link]
Rostami, M., Bakhtiarpour, S., & Hafezi, F., Naderi, F. (2023). Investigating the effectiveness of verbal self-education training on academic procrastination and symptoms of attention deficit/hyperactivity disorder in adolescent boys with attention-deficit/hyperactivity disorder. Practice in Clinical Psycholoy, 11(2), 141-150. [DOI:10.32598/jpcp.11.2.851.1]
Saha, T. K. (2009). War on word in cyberspace legal constraints and conflicts between right of privacy and freedom of speech. Journal of Intellectual Property Rights, 14, 489-500. [Link]
Seder, P., & Oishi, S. (2009). Ethnic/racial homogeneity in college students’ Facebook friendship networks and subjective well-being. Journal of Research in Personality, 43(3), 438-443. [DOI:10.1016/j.jrp.2009.01.009]
Shapira, N. A., Goldsmith, T. D., Keck, P. E., Jr, Khosla, U. M., & McElroy, S. L. (2000). Psychiatric features of individuals with problematic internet use. Journal of Effect Discord, 57(1-3), 267-272. [DOI:10.1016/s0165-0327(99)00107-x.] [PMID]
Sharif, S. P., & Khanekharab, J. (2017). Identity confusion and materialism mediate the relationship between excessive social network site usage and online compulsive buying. Cyberpsychology, Behavior, and Social Networking, 20(8), 494–500. [DOI:10.1089/cyber.2017.0162] [PMID]
Suleiman, M. M., & Sani, S. (2020). Social networks as integral of ICT: A predictor of academic procrastination. KIU Interdisciplinary Journal of Humanities and Social Sciences, 1(2), 229-241. [DOI:10.59568/KIJHUS-2020-1-2-16]
Steers, M. N. (2016). ‘It’s complicated: Facebook’s relationship with the need to belong and depression. Current Opinion in Psychology, 9, 22-26. [DOI:10.1016/j.copsyc.2015.10.007]
Su’arez-Perdomo, A., Ruiz-Alfonso, Z., & Garc’es-Delgado, Y. (2022). Profiles of undergraduates’ networks addiction: Difference in academic procrastination and performance. Computers & Education, 181, 104459. [DOI:10.1016/j.compedu.2022.104459]
Ng, T., Sanders, H., Merrill, S., & Faustin, M. (2024). Media's effect on athletes' mental health. Clinics in Sports Medicine, 43(1), 187–198. [DOI:10.1016/j.csm.2023.06.022] [PMID]
Sabzi, N., Farah Bijari, A., & Khosravi, Z. (2022). The effectiveness of group schema therapy-based parenting education of mothers on modifying maladaptive schemas of the child and improving the quality of the parent-child relationship. Practice in Clinical Psycholoy, 10(1), 33-44. [DOI:10.32598/jpcp.10.1.798.1]
Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is Facebooking depressing? Computers in Human Behavior, 43, 139-146. [DOI:10.1016/j.chb.2014.10.053]
Tateno, M., Teo, A. R., Shiraishi, M., Tayama, M., Kawanishi, C., & Kato, T. A. (2018). The prevalence rate of Internet addiction among Japanese college students: Two cross-sectional studies and reconsideration of cut-off points of Young’s Internet Addiction Test in Japan. Psychiatry and Clinical Neurosciences, 72(9), 723-730. [PMID]
Teclehaimanot, B., & Hickman, T. (2011). Student-teacher interaction on Facebook: What students find appropriate. Tech Trends, 55(3), 19-30. [DOI:10.1007/s11528-011-0494-8]
Thompson, L. A., Dawson, K., Ferdig, R., Black, E. W., Boyer, J., & Coutts, J., et al. (2008). The intersection of online social networking with medical professionalism. Journal of General Internal Medicine, 23(7), 954–957. [DOI:10.1007/s11606-008-0538-8] [PMID]
Tyagi, T., & Meena, S. (2023). Online social networking and its relationship with mental health and emotional intelligence among female students. Clinical Epidemiology and Global Health, 17, 101131. [DOI:10.1016/j.cegh.2022.101131]
Uddin, M., Mamun, A., Iqbal, M., Nasrullah, M., Asaduzzaman, M., & Sarwar, M., et al. (2016). Internet addiction disorder and its pathogenicity to psychological distress and depression among university students: A cross-sectional pilot study in Bangladesh. Psychology, 7(8), 1126-1137. [DOI:10.4236/psych.2016.78113]
Vosoughi Motlagh, A., Kamjou, S., & Etemaad, J. (2023). Predicting body image concerns, social isolation, and mood by the amount of social media addiction. Practice in Clinical Psycholoy, 11(4), 297-306.[DOI:10.32598/jpcp.11.4.856.1]
Woods, H. C., & Scott, H. (2016). #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression, and low self-esteem. Journal of Adolescent, 51, 41-49. [DOI:10.1016/j.adolescence.2016.05.008] [PMID]
Yedidia, M. J., Gillespie, C. C., Kachur, E., Schwartz, M. D., Ockene, J., & Chepaitis, A. E., et al. (2003). Effect of communications training on medical student performance. JAMA, 290(9), 1157–1165. [PMID]
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237-244 [DOI:10.1089/cpb.1998.1.237]
Type of Study: Research | Subject: Analytical approach
Received: 2023/12/10 | Accepted: 2024/02/14 | Published: 2024/04/1

Add your comments about this article : Your username or Email:

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Designed & Developed by : Yektaweb