- To investigate the socio-demographic characteristics and social media usage of youth.
- To find out the mean differences regarding social media and mental health with respect to the gender of the subjects.
- To analyze the relationship among socio-demographic profile, social media and mental health of youth.
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- Malik, S., & Khan, M. (2015). Impact of facebook addiction on narcissistic behavior and self-esteem among students. J Pak Med Assoc, 65(3), 260-263.
- Maree, T. (2017). The social media use integration scale: Toward reliability and validity. International Journal of Human- Computer Interaction, 33(12), 963-972.
- Pakistan Social and Living standard measurement (2013-14).
- Qadeer, N. (2016). A start of something big. MIT technology Review Pakistan.
- Saleem, M., Irshad, R., Zafar, M., & Tahir, M. A. (2016). Facebook addiction causing loneliness among higher learning students of Pakistan: a linear relationship. Journal of Applied and Emerging Sciences, 5(1), pp26-31.
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Abstract:
Excessive social media usage can be harmful for to the mental health of individuals. The core objective of the study was to inspect the effects of social media usage on mental health. Secondly, to find out the mean differences of gender, social media and mental health. A cross cross-sectional research design was utilized to fulfill the study objectives. n=1067 students from six different universities in Punjab, Pakistan, were selected through a multi-stage sampling technique. Data was collected through an adapted questionnaire comprised of three distinct parts, i.e. socio-demographic profile, social media integration scale by Maree (2017) and Goldberg depression scale (1993), to check the social media effects on mental health among youth. Attained replies were analyzed by using SPSS analysis software version 21. Furthermore, to check the direction and consistency of the relationship among these variables, Pearson correlation was applied, which demonstrated a negative relationship between social media usage and mental health problems at p<.000. This study concluded a negative influence of excessive social media usage on mental health among youth in public universities of Punjab, Pakistan.
Key Words
Social Media, Youth, Mental Health, Pakistan, Gender
Introduction
For decades, social media has been understood as a producer of imbalance for the scholars, parents and society in terms of the mental health of human beings around the globe. Excessive usage of social media is now the most conjoint activity among the youth of the present time. All the application software’s allow interaction through 2.0 and 3.0 websites. These software applications are most probably Facebook, Twitter, Online gaming, YouTube, and WhatsApp (Bashir & Bhat, 2017). These social media applications allows individuals to communicate with their closed ones anytime and anywhere all over the world with a signal click. Among the aforementioned social media applications, only Facebook report about 1 billion active users during the year of 2015, which means that 1/7 people on earth use Facebook every day to get connected with friends and family. Apart from digital communications, social media has a significant influence on the other aspects of people, especially youth. Poor understanding is mainly a graving grave concern in the younger population. It is because when teenagers and youngsters spend their good amount of time on social media, they are at higher risk of negative influences (The Hearty soul, 2016).
According to a recent cohort study of Kelly et al. (2018), regardless of the valuable usage of social media for communications and other purposes, it is highly associated with anxiety, stress, depression and poor mental health problems among youth. Moreover, it has been proven that females are more vulnerable to face mental health problems than males due to social media. Growing A growing body of literature, Fahy et al. (2016) directed in the intervening pathways related to youngsters mental health with respect to the time they spend on using social media, and the way they enroll themselves with the others. Widely concerned are the domains where premature people face online harassment as a victim/offender, which could have the potential to influence their mental health due to easy accessibility of sharing materials which can damage their reputation and companionships (Tokunaga, 2010). By following the assumptions of the Co-construction theory, it has been postulated that social media allows people to construct their own reality, post, like and dislike whatever they like to do. Theoretically, young people who perceive less encouragement and experience difficulties in seeking attention might suffer from a sense of loneliness, hopelessness, panic, or anxiety and might prefer associations with others via unforeseen setups (Barry et al., 2017).
Several studies demonstrate the relationship between increased online time spent on social media and grown grown-up youths mewntleal diturbamce disturbance and stress. For example, Woods & Scott (2016), Viner et al. (2019). Singnificant relations were found between social media usage and people mentle mental health in a good number of research papers with the limelight, i.e. “Have smartphones destroyed a generation?” And furthermore, “social media connected to increased mental health problems” (Coyne et al.2020). In Pakistan, a study conducted by Zaheer (2018) found that both traditional and social media are considered and criticized for developing irresponsible and unethical behavior among users. It has been perceived that social media is developing a sense of alienation among youth from national, cultural and Islamic values, which later badly affect the overall well-being of youth. Furthermore, in the study of Saleem et al. (2016), the relation of a Facebook addiction and loneliness was negative, low self-confidence, narcissism, depression, and underprivileged social life among students (Malik & Khan, 2015; Mehmood, Jafree & Sohail, 2020). Similarly, according to the world disease ranking, mental health is the predictor of five in ten diseases in Pakistan, and there is an alarming need to address this issue to avoid serious outcomes. Little empirical studies have been taken place to investigate the relationship between social media usage and the mental health of youth in a highly populated province of Pakistan, i.e. Punjab. Henceforth, the present study fulfills the study gap regarding the relationship of between social media usage and the mental health of young in Punjab. Furthermore, it gives drawbacks regarding the role of gender and other associated factors with the mental health of youth in public universities of Punjab, Pakistan. Below are the objectives of the current study:
Objectives
Literature Review and Construction of Hypothetical Model
Pakistan is the 6th most populous country around in the world, with 170 million citizens. Social media is a speedily flattering part of human lives in Pakistan, and especially the availability of internet revolution in cellular diligence affects the lives to a very greater extent. In the past few years, and unparalleled rise have been observed in Pakistan regarding usage of the internet and particularly social media websites. It has become popular among people of all ages, and people use it through mobile phones, I-pads and laptops frequently. Overwhelmingly, people use social media sites for communication and connectivity. Qadeer (2016) affirmed about 10 core users of mobile, and approximately 2.9 core consumers of the internet, and out of these figures, nearly 1.4 core Pakistani use the internet via a different mode of mobile. A as Pakistan is a patriarchal society, so males statistics of social media usage are found higher than females. It is worthy to note that Pakistan is one of those countries who that have higher statistics of 62% youth aged 18-24 year (Zaheer, 2018). Social media has both positive and negative effects on the well-being of youth, especially students, during their university tenure as it enhances the mechanism of social capital and support. Furthermore, it can also guide students regarding academic content and provide a platform to establish social relations. Conversely, excessive usage of social media can direct social media addiction which later leads toward mental health problems (Mahmood, Jafree & Sohail, 2020). Another leading negative consequence lies in the foundations of the Social comparison theory of Leon Festinger (1954). Main The main component and argument of the theory are that folks have the instincts of comparing their lives with the lives of others. These foundations are indicating one’s judgement regarding one’s self, thus leading to negative outcomes such as mental health problems. The users of social media websites, especially youngsters, are fond of presenting the best version of themselves. Consequently, browsing on others’ posts leads the watching to perceive the lives of other people better than them. It further leads them to establish a negative image regarding themselves and increase mood swings. Growing A growing body of preceding literature directed that comparison of self with the others is linked with spending more time on social media and mental health problems among youth (Holland & Tiggemann, 2016). A Croatian study found that excessive usage of social media was parallel with depression. Findings affirmed that the more the student uses time in online space while online activities, the more will they be depressed hence causes mental issues.
Hypothetical Model of the Study
H1: There is a significant influence of gender on social media usage and mental health among the youth of public universities in Punjab, Pakistan.
H2: Level of education and place of residence are significantly associated with social media usage and the mental health of youth.
H3: There is a negative correlation between social media and mental health among the youth of public universities in Punjab, Pakistan.
Material and Methods
University structural pathology of higher education commission of Pakistan (2014) directed 26 public universities in thirty-six districts of Punjab, Pakistan. By focusing on these conditions, the authors designed the current study.
Participants and Procedure
A total number of n= 1067 participated in the present study from n=6 public universities of study location, i.e. the university University of Gujrat, University of Agriculture, Bahauddin Zakariya universityUniversity, Multan, Islamia University, Bahawalpur, the University of Sargodha and University of Punjab, Lahore. Students of both natural and social sciences were eligible for the study, but these were only those subjects who at least have passed one semester of BS or masters and currently registered for one month in their respective program. At first, the researcher dispatched a consent letter in the classrooms of the targeted populous for receiving their willingness to participate in the study. Researcher The researcher selected the targeted population through a multi-stage sampling technique in command to approve panorama, generalizability and accurateness of the answers. Firstly, the authors randomly selected n=6 renowned districts among 36 districts of Punjab. Secondly, the researcher selected one public university among all the selected districts and approached the study subjects through a simple random sampling technique. The total population of masters and BS students of these universities was 108699, and the sample size was determined by using Cochran’s formula (1963) for sample size determination. Henceforth, the sample size for the current study was n=1067.
(z_(?/2)^2 ?^2 )/d^2
n= (?(1.96)?^2 ?(0.5)?^2)/((?0.03)?^2 )
n= ((3.8416)(0.25))/0.0009
n= 0.9604/0.0009= 1067.11 or 1067
Measurements
Students reported mental health through social media usage. For this purpose, researchers segregated the questionnaire into three distinct parts, i.e. 1) Socio-demographic profile, 2) Independent construct (Social media usage), and 3) Dependent construct (Mental health). For measuring socio-demographic profile, the researcher used questions regarding age, gender, level of education, parental education, parental work sector, place of residence, family type, preferred social media websites, number of friends on social networking sites, the average period spent on social media. These constructs were retrieved from Pakistan social and living standard measurement (2013-14). Consequently, Social media usage was used as an independent construct. For measuring social media usage among the subjects, the researcher adapted adopted the social media use integration scale (Maree, 2017) prior used by Berryman, Ferguson and Negy (2018), comprised of ten questions measuring the emotional value of social media on the lives of subjects. Responses were recorded through 5 points Likert scale 1=strongly disagree, 2=Disagree, 3= Neutral, 4= Agree and 5= strongly agree. Similarly, mental health was measured through Goldberg depression scale (1993), comprised of nine items measuring mood and feelings globally used for measuring mental health of the subjects regarding their behavior in the past week. Each item in the scale was linked with the next item. These items were recorded through a 5-point Likert scale ranging from 1=, not at all, 2= not really, 3= undecided, 4= somewhat, and 5= very much.
Statistical Analysis
Data managing and analysis software SPSS version 21 was utilized for coding, transforming and recording the study constructs. Firstly, assessment of the subject’s socio-demographic profile was determined through frequency and percentage. Later, the association between independent constructs and the dependent construct was assessed by using an independent sample t-test to check either the mean differences exists among understudied independent gender and place of residence of the youth and dependent variable social media usage and mental health problems. Furthermore, the relationship between social media usage, mental health, and gender, level of education and place of residence was analyzed through the Pearson correlation coefficient.
Data Analysis
Descriptive Statistics
This section is comprised of frequency (f) and percentage (%) of items which that were asked to determine socio-economic status, demographic characteristics and preferred social media sites along with time spent on social media of targeted subjects.
Table 1. Distribution of the Study Subjects with Respect to Socio-Demographic Profile and Social Media Usage
| Items width="208" valign="top">Categories width="208">f(%) | > Age width="208" valign="top">18-20 width="208">85(8) | > width="208" valign="top"> 21-23 width="208">498(47.7) | > width="208" valign="top"> 24-26 width="208">342(32.1) | > width="208" valign="top"> 27 and above width="208">142(13.3) | > Gender width="208" valign="top">Male width="208">417(39) | > width="208" valign="top"> Female width="208">651(61) | > Level of education width="208" valign="top">BS width="208">470(44) | > width="208" valign="top"> Masters width="208">597(55.9) | > Father’s Qualification width="208" valign="top">Illiterate-Primary width="208">190(17.8) | > width="208" valign="top"> Middle-Matriculation width="208">153(14.3) | > width="208" valign="top"> Intermedia-Bachelors width="208">519(48.6) | > width="208" valign="top"> Masters width="208">149(14) | > width="208" valign="top"> Above width="208">56(5.2) | > Father’s work sector width="208" valign="top">Government width="208">240(22.5) | > width="208" valign="top"> Semi-government width="208">191(17.9) | > width="208" valign="top"> Private width="208">122(11.4) | > width="208" valign="top"> Unemployed width="208">50(4.7) | > width="208" valign="top"> Other width="208">464(43.5) | > Mother’s Qualification width="208" valign="top">Illiterate-Primary width="208">371(34.7) | > width="208" valign="top"> Middle-Matriculation width="208">288(26.9) | > width="208" valign="top"> Intermedia-Bachelors width="208">303(28.3) | > width="208" valign="top"> Masters width="208">63(5.9) | > width="208" valign="top"> Above width="208">42(3.9) | > Mother’s work sector width="208" valign="top">Government width="208">56(5.2) | > width="208" valign="top"> Semi-government width="208">112(10.5) | > width="208" valign="top"> Private width="208">71(6.7) | > width="208" valign="top"> Unemployed width="208">206(19.3) | > width="208" valign="top"> Housewife width="208">622(58.3) | > Place of residence width="208" valign="top">Rural width="208">435(40.8) | > width="208" valign="top"> Urban width="208">632(59.2) | > Family type width="208" valign="top">Extended width="208">155(14.5) | > width="208" valign="top"> Joint width="208">440(41.2) | > width="208" valign="top"> Nuclear width="208">472(44.2) | > Monthly family income width="208" valign="top">Below 20000PKR width="208">176(16.5) | > width="208" valign="top"> 20000PKR-40000PKR width="208">183(17.2) | > width="208" valign="top"> 41000PKR-60000PKR width="208">373(35) | > width="208" valign="top"> 61000PKR and above width="208">335(31.4) | > Overwhelming used website width="208" valign="top">108(10.1) | > width="208" valign="top"> Snap Chat width="208">107(10) | > width="208" valign="top"> 142(13.3) | > width="208" valign="top"> 345(32.3) | > width="208" valign="top"> YouTube width="208">162(15.2) | > width="208" valign="top"> TikTok width="208">56(5.2) | > width="208" valign="top"> 98(9.2) | > No. of friends on social media width="208" valign="top">100-200 width="208">286(26.8) | > width="208" valign="top"> 210-300 width="208">248(23.2) | > width="208" valign="top"> 301-400 width="208">176(16.5) | > width="208" valign="top"> 401-500 width="208">183(17.2) | > width="208" valign="top"> 500 and above width="208">174(16.3) | > Time spent on social media width="208" valign="top">1-2 hour width="208">134(12.6) | > width="208" valign="top"> 2-3 hour width="208">262(24.6) | > width="208" valign="top"> 3-4 hour width="208">180(16.9) | > width="208" valign="top"> 4-5 hour width="208">142(13.3) | > width="208" valign="top"> 5-6 hour width="208">176(16.5) | > width="208" valign="top"> 7 and above width="208">173(16.2) |
| Variables width="71" rowspan="2" valign="top">Categories width="41" rowspan="2">M width="35" rowspan="2">SD width="47" rowspan="2">Df width="83" rowspan="2">Sig(2-tailed) width="204" colspan="2">95% confidence interval of the difference | > Lower width="102">Upper | > SM width="71" valign="top">Male width="41">43.9 width="35">4.04 width="47">1062 width="83">.055 width="102">-.405 width="102">.753 | > Female width="41">43.77 width="35">5.05 width="47">872.4 width="83">.053 width="102">-.377 width="102">.725 | > MH width="71" valign="top">Male width="41">28.9 width="35">2.41 width="47">1062 width="83">.047 width="102">-.876 width="102">-.005 | > Female width="41">29.4 width="35">4.07 width="47">1045.5 width="83">.027 width="102">-.831 width="102">-.050 | > SM width="71" valign="top">Rural width="41">44.4 width="35">4.89 width="47">1065 width="83">.001 width="102">.368 width="102">1.50 | > Urban width="41">43.4 width="35">4.49 width="47">879.4 width="83">.002 width="102">.359 width="102">1.51 | > MH width="71" valign="top">Rural width="41">29.7 width="35">3.07 width="47">1065 width="83">.000 width="102">.373 width="102">1.23 | > Urban width="41">29.8 width="35">3.77 width="47">1034.2 width="83">.000 width="102">.388 width="102">1.21 |
| Variable width="60">M width="59">SD width="52">1 width="52">2 width="54">3 width="53">4 width="60">5 width="75">6 | > SM width="60">43.84 width="59">4.68 width="52">R align="center">P width="52">1 width="54">-.208** align="center">.000 width="53">-.008 align="center">.782 width="60">-.079** align="center">.010 width="75">.-98** align="center">.001 | > MH width="60">29.26 width="59">3.52 width="52">R align="center">P width="52">width="54"> 1 width="53">.054 align="center">.077 width="60">-.091** align="center">.003 width="75">-.112** align="center">.000 | > Gender width="60">1-62 width="59">.503 width="52">R align="center">P width="52">width="54"> width="53"> 1 width="60">.215** align="center">.000 width="75">.205** align="center">.000 | > LoE width="60">2.18 width="59">.919 width="52">R align="center">P width="52">width="54"> width="53"> width="60"> 1 width="75">.181** align="center">.000 | > PoR width="60">1.59 width="59">.492 width="52">R align="center">P width="52">width="54"> width="53"> width="60"> width="75"> 1 |