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Mental health is integral and intrinsic part of human survival. It plays important role from the individualistic apprehension to societal perspective. At present, it became essential integer for achieving the sustainable development. The phrase ‘Healthy brain lies in healthy body’ emphasized the role of health in growth and developmental aspect from family to nation. At the start of 21st century, mental health is included in definition of disease by World Health Organization. This aspect became fundamental by emphasizing the good mental health through WHO Mental Health Action Plan 2013-20 in 65th World Health Assembly which is re-instated as tag line of ‘Transforming Mental Health for All’ in World Mental Health Report (2022). Mental health traits gained momentum during and post Covid pandemic due to unprecedented lockdown, restrictions and isolation. Mental health is not merely ailment but is a complex array of mental dismay to psychosocial disabilities which manifest themselves in diverse approach to person to person which is perceived individually with altering extent of difficulty, distress and impending socio-clinical outcomes (WHO, 2022). Anxiety, depression and stress are most common manifestation of mental disorders and are induced due to unfavourable social, economic, geopolitical and environmental circumstances which include poverty, violence, inequality, perceived injustice, rejection, discrimination and environmental deprivation (Galderisi et al., 2015). Risk factors associated with mental ailments can mark these anarchy at all stages of life, but all those induced at impulsive childhood are predominantly detrimental. Some inevitable and regrettable circumstances like cruel parenting, substantial punishment, emotional blackmailing, discrimination and bullying in early stage may augment mental health conditions among adolescents (Ahn, 2011; Slade et al., 2014). Globally, majority of adolescents faced moderate to severe mental ailment which are usually unrecognizable so untreatable. World Health Organization reported 86 million adolescents aged 15–19 and 80 million adolescents aged 10–14 have mental health issues. Gender specific study reported 89 million adolescent boys and 77 million adolescent girls aged 10–19 live with a mental disorder. Adolescents experienced aberrant deaths in which suicide stands fifth which increased in late adolescents up to fourth. These conditions may induces estimated annual loss of US$387.2 billion in terms of human capital as per purchasing power parity (WHO, 2012; 2013; UNICEF, 2021). Mental issues were found to increase at alarming rate of 52% during period of 2005-2017 which indicated that adolescents are vulnerable and easily victimized due to their physiological, psychological and social enigma (Twenge et al., 2019). Social media imparts new dimensions as their widespread and inevitable contact to adolescents added a new facet to the mental health. 78.05% population possessed smart-phone among which more than one mobile are held by many persons by the end of 2020. The smart-phone subscriptions as of 2021 are supposed to be 6.23 billion which exceed the number of smart-phone users (Laricchia, 2023). Statista informed that global number of social media users are 4.76 billion having penetration rate of 59.4% having a major purpose of connection with family and friends while others reasons are filling spare time, looking for inspiration, reading and creativity. Social media usage indicated differential daily performance as about 60 minutes for adults, while 49.12 minutes among adolescents (Dixon, 2022). Depression is correlated with upsurge of social networking sites browsing during several cross-sectional and co-relational surveys (Brunborg and Andreas, 2019; Ivie et al., 2020; Keles, McCrae, and Grealish, 2020). Even though, several studies reported about the beneficial outcomes of social media which are supposed to be of greater value, many researchers alarmed about the negative impact of social media (Elsayed, 2021). A few studies underline the any significant correlation amid social media usage and depressive sign (Coyne et al., 2020). Majority of correlational and cross-sectional studies are confined to measure the impact of time spent on social media with mental health indicators and withdraw the inter-relation between anxiety and depression with social networking sites. Some workers highlighted the role of gender and individual psychological well-being in onset of depression and anxiety (Ahn, 2011; Calandri, Graziano and Rolle, 2021). In light of the views, a randomized gender specific survey based study is planned to compare the use of social network with induction of mental health. Non-users of social media are treated as control and separately evaluated for the statistical variation with proposed null hypothesis as ‘effect of social media usage is not associated with mental ailments’. The alternate hypothesis accepts the role of social media use in development of mental disorders.

Social media is new emerging phenomenon of present century which manifest itself in ubiquitous manner having deep reach in every aspect of life. Frequent use of smartphone in general and social media specially may induces disturbances slowly which cannot be judged in early stage and only be understood when it is very late. Role of social media is highlighted in many progressive aspects ranging from study to personality development to peer recognition but certain negative aspects are surfaced which make it as an inevitable nuisance.  A gender specific study reported that girls manifested the mental disorders in differential symptoms as compared to commonly recognised set of symptoms which encompasses most common unexplained fatigue decreased energy, psychomotor changes and lack of concentration, weight change and suicidal tendency (Khalil, 2010). Social networking addiction may be potential danger which is accountable for the adolescents’ mental health and psychological problem in behaviour dependent manner. Extroverts present themselves as social enhancers while introverts exhibited narcissism which decreases real life community communication, low academic performance, relationship thrash and compulsive drug abuse (Kuss and Griffiths, 2011).

Sarda et al. (2013) found that gender, age, economic condition and urban and rural background do not play any noteworthy role in beginning and development of mental disorders. As guilty verdict, it is supposed that social anxiety is prevalent in society to varied intensity and became a motive for depression which negatively effects physical and psychological difficulty but no correlation between social media use and social anxiety are reported. Although the existence of social anxiety are not denied but highlighted that other variables are hidden in social dispersion which may be root cause for the beginning of anxiety and depression (Riaz, Ishaq and Abbasi, 2018). Social media are not found of permanent and stable effect on life satisfaction of adolescents in longitudinal, cross-sectional, randomised-intercept, specification curve analysis. Mostly effects are statistically insignificant with indication of more susceptibility among girls (Orben, Dienlin and Przybylski, 2019). The results revealed that prolonged social media use endorsed elevated depressive symptoms along with lower well-being and life satisfaction among girls of lower emotional self-efficacy and vice-versa (Calandri, Graziano and Rolle, 2021). Achmad et al. (2023) underline the significance of socialization, healthy and wise use of social media which provide positive and remarkable influence on the lifestyle of adolescents in the city of Bandung by 43% at the cost of disturbed sleep pattern.

General correlation amid social media use and mental health problems are observed along with some confounding variables (e.g. age and gender) and intervening factors (e.g. insomnia, rumination and self-esteem) in the studies where results are not utterly consistent (Keles, McCrae and Grealish, 2020). 50% non-dependent, 8.3% moderate and 41.7% dependent respondents are reported in descriptive phrase, questionnaire based, convenient sampling which exhibited unconstructive effect of social media but dependent user might be border line addicts (Kurian et al., 2021). Social media use and playing games were positively associated with internalizing symptoms which are conditional on gender indicating girls might be especially vulnerable to display internalizing symptoms and different kinds of social media activities with sense of well-being (Svensson, Johnson and Olsson, 2022).

Pantic et al. (2012) reported direct association between depression and online social networking as high usage of social sites accelerate the depressive symptoms and reduce the sleep time of the adolescents. Overall, psychopathology may not be influenced by social network usage but subjective well-being of the user is primary concern. Impoverished social network and changes in mental condition are linked with adolescents with mental illness which ultimately leads towards change in individual profiles. Thus screening the substance and worth of interaction is good marker for the recognition of mental issues in early phase (Seabrook, Kern and Rickard, 2016). A controlled descriptive study on 90 respondents with respect to demography, social networking usage, social relationship and health affects which indicated that significant links and affirmative correlations between social media usage and feeling of anxiousness on social networking sites (Deepa and Priya, 2020). The confirmed psychological effects of social media use among students contain depression, stress, anxiety, emotional isolation, low self-esteem, memory loss, and self-harm (Olola, Asukwo and Odufuwa, 2022). Correlational study on addiction showed that the gender specific findings as 58% females among 11% of the total participants are extensively addicted to social media having low self-esteems which prompts higher social media surfing and playing video-games as distractive measures (Ciacchini et al., 2023).

During the study, Labrague (2014) find the indirect link between Facebook usage and mental disorder. Although the Facebook itself cannot induce negative emotions but prolonged use raises the depression and anxiety indicators and troubled emotional situation of the subjects. Facebook is not only helps in relationship continuance, time surpass, amusement and friendship but for flight from negative phase and mood upliftment. Usually excessive users might face habit forming and slowly became compulsive and addictive. It appeared that frequency of use, duration of use and content of use might be associated with surveillance gratification, entertainment gratification and content gratification and play prominent role in Facebook addiction in gender specific manner as females prefer to maintain their existing friend base but males always expand to new users. Facebook addiction occupies various factors namely low psychological well-being, loneliness, non-socially motivated use, fear of missing out (Ryan et al., 2014). The association between social media and academic performance is found in terms of significant deviation of test score of mathematics with respect to social media usage. Students with history of social media usage less than an hour have superior performance as compared to subjects with usage of more than 7 hours (Igcasama et al., 2019).

In present scenario, study of psychological well-being and social capital must be incorporated along with some randomised control entities which do not have any social media account for more relatable and statistically valid findings. Gender should also be incorporated in study; more study should be planned on girls to understand the effect better.

Survey: A randomised sampling process were performed in various department of Dr. H.S. Gaur Sagar Unioversity, Sagar, M.P. and KV, Sagar from 2021-2023. Internal subsampling will be used to reduce respondent burden by dividing the study into 2 parts. Part 1 will be included core diagnostic assessment. Part 2 will be included subjects which are non user of social media and are treated to be control for the study. Total 60 girl students up to age of 19 were counselled and briefly explained about the nature of study. After that they were voluntarily asked for the filling of the different formats for evaluation of Facebook Intensity, Anxiety and Depression. Total 3 formats were used, any ambiguity or confusion about questions was duly addressed and the participants were prompted to fill and return all formats within week.

The collected data was checked for completeness and cleaned using Microsoft Excel by removing all incomplete responses so that statistical analysis of the data could be done. Cronbach’s alpha was used to the determine reliability of all constructs within the study, with numbers >0.7 taken to indicate reliability (Tavakol and Dennick, 2011). All values are reported as mean ±SD. The Student’s t-Test was used for analysis of variance in between users and non-users. Correlation coefficient was used to measure the impact of social media on adolescents as required.

Demographic information exhibited in Table 1 which revealed that 86.67% recovery rate of the questionnaire. The average age of subjects is 17 years. Television time is 1.24 hours while average time spent on internet 2.14 hrs.76.92% users (40 girls) have Facebook account with average member of 82.13 while 67.41% have alternate or additional social accounts. Usually subjects spent 2.38 hours average on social networks with some members have very high internet usage of 10 hours (Table 1). 22 to 33% respondents don’t have Facebook or any other social network membership and these are treated as control for the study.

 Usually Facebook users spent 10 min. to 3 hours for the social network surfing (Table 2). It is also clear that subjects with high numbers of online friends spent more time on social media and internet (data not shown). Usually members showed the Facebook intensity value of 2.79 which exhibited average to strong bonding with social networking sites. Data expressed in terms provide good reliability with Cronbach’s alpha value 0.77342. Girl prefers to connect with person they already known to him like classmate, neighbour or friend (average value of 2.93) as compared to new people having average value of 2.4 (Table 3). The trend indicated that they prefer to share the thought, media and other content with offline friends. Respondents valued their old friendship most with index value of 4.15.

Table 4 depicts about the psychological well being in terms of self-esteem. The performances of respondents are compared in between users and non-users. The non-user self-esteem is slightly high with mean value of 3.85 as compared to users having value of 3.41 (Cronbach’s alpha is 0.81 and 0.73 respectively). The value indicated that non-users have high self-esteem but Student’s t-Test showed insignificant variation of 0.2865 at 5% significance.

Social capital is evaluated in terms of bridging social capital and bonding social capital. Bridging social capital evaluates the tendency to link external asset for the information resources which indicated the weak bonding pattern among the created group. In this scenario group search information externally and rely on other sources thus indicated as weak or loose network. On the other hand bonding social capital involved the likeminded people in a group which strengthen the group and behave like a closed or tight network. During survey both parameters are assessed among both user and non-user. Social media user exhibited low degree of bridging social capital having mean value of 17.18 as compared to non-users with value of 18.42. Although, these values are again insignificant (t = 0.359776, p < 0.05) but non-users prefer to broad their knowledge base form other external sources. On the other hand, bonding social capital revealed inverted trend as non-users have low degree of asset value (8.92) as compared to users with high asset value (9.78). in this scenario, it can be concluded that users have high degree of bonding in terms of resource distribution apparently as the physical variation is not statistically significant (t = 0.207371) at 5% significance (Table 5).

Table 6 exhibited the data related to mental disorders. In the table anxiety and depression are presented separately for users and non-users. The anxiety is sub-classified in 5 variants namely; panic disorder, generalised anxiety disorder, separation anxiety disorder, social anxiety disorder and significant school avoidance. The thresh hold value for anxiety is 1-25 as normal, 26-29 as moderate and <30 as severe and specific anxiety. Depression indicator is 1-15 normal, 16-21 is moderate and <22 is confirmed depression. The survey data exhibited that non-users of social media do have equal or high degree of disorders despite the use of social media. Among non-user 66.67% respondents faced severe anxiety while 25% faced severe depression as compared to users where 50% severe anxiety and 37,5% severe depression is documented. 77.5% users have found to be associated with separation disorder while 70% users have panic disorder. Among non-users separation, panic and generalised anxiety disorders are found at very high level (88.33%, 75% and 75% respectively). In this scenario, it can be proposed that only social media cannot be blamed for the induction of mental disorder. There must be certain other facets which are involved in theses parameters.

Finally Table 7 provides the correlational data which interconnect the different parameters with respect to anxiety and depression. The main parameters taken are Television Exposure (in hrs), Internet Surfing (in hrs), Facebook Members, Facebook Intensity, Average Calculated Intensity, Facebook Usage Pattern, Psychological Well-being, Bridging Social Capital and Bonding Social Capital. All these parameters are supposed or claimed to be responsible for triggering of the mental illness. In the study none of the parameters are supposed to induce any disorder at high level of correlation at any significance level. Only Facebook intensity (cumulative of total member and usage time) and average Facebook intensity indicate the slight low negative correlation with anxiety (-0.41834 and -0.41147 respectively) for social media users while TV exposure time indicated low positive correlation among non-users (0.424559). On basis of these results, the relation among social media use and mental illness cannot be proved. They may be guiding force but at very low level and alone they cannot induce any significant change in interrelation of the both parameters. The social media might have implicated the psychological and social well being among users as compared to non-users. The findings suggested low positive for anxiety (psychological well being 0.021154 for users and -0.27012 for non-users) to negligible indicator for depression although the correlation coefficient indicated insufficiency but trend is pleasant. Non-users exhibited weak positive for the bonding social capital (0.428535) indices which suggests that they rely on internal source for their information and knowledge base.

 Table 1: Sample Demography

	Total Number/ Max Value	Recovery/ Min Value	Percentage/ Mean	Standard Deviation
Recovery Rate	60	52	86.67 %	-
Age	19 Years	14 Years	17 Years	1.27
Television Usage	3 Hours	0 Hour	1.24 Hours*	0.79
Internet Usage	10 Hours	0 Hour	2.14 Hours*	1.83
Social Accounts (Facebook)	40 (Users)	12 (Non-users)	76.92 %	-
Other Social Media	35 (Users)	17 (Non-users)	67.41 %	-
Social Sorority	410 (Members)	5 (Member)	82.13 (Member)#	73.80
Time on Social Networks	>3 Hours	<10 Min	2.38#	1.23

^*Converted value from Lickert scale (mid value of defined range is used), for example Television/Internet usage of 2-3 hours are treated as 2.5

^# Members/Time on social network on nominal scale where 2 indicated as 31-60 min of usage and 3 indicated as 1-2 hours usage

Table 2: Facebook Intensity

S.N.	Parameter	Mean Response	Standard Deviation
1.	About how many total Facebook friends do you have?	2.05	1.99
2.	In the past week, on average, approximately how many minutes per day have you spent on Facebook?	2.38	1.23
3.	Facebook is part of my everyday activity	2.68	1.14
4.	I am proud to tell people I’m on Facebook	2.95	1.60
5.	Facebook has become part of my daily routine	2.78	1.62
6.	I feel out of touch when I haven’t logged onto Facebook for a while	2.70	1.59
7.	I feel I am part of the Facebook community	3.10	1.69
8.	I would be sorry if Facebook shut down	2.53	1.4
	Total Intensity (Cronbach’s alpha =  0.77342)	2.79	1.07

^*Converted value from Lickert scale (mid value of defined range is used), for example Television/Internet usage of 2-3 hours are treated as 2.5

^# Members/Time on social network on nominal scale where 2 indicated as 31-60 min of usage and 3 indicated as 1-2 hours usage

Unless otherwise stated, the response recording made from 1 (Strongly disagree) to 5 (Strongly agree)

Table 3: Facebook Usage Pattern

S.N.	Parameter	Mean Response	Standard Deviation
A	Off to On line (connection on Facebook with known contacts )	2.93	0.95
1.	I have used Facebook to check out someone I met socially	2.70	1.57
2.	I use Facebook to learn more about other people in my classes	2.48	1.63
3.	I use Facebook to learn more about other people living near me	2.40	1.41
4.	I use Facebook to keep in touch with my old friends	4.15	1.17
	Total Usage (Cronbach’s alpha =  0.54)	11.73	3.78
B	On to Off line (connection on Facebook with unknown contacts )
1.	I use Facebook to meet new people	2.40	1.86
	Single option agenda

Unless otherwise stated, the response recording made from 1 (Strongly disagree) to 5 (Strongly agree)     

Table 4: Psychological Well-being

S.N.	Parameter	Active on SNS		No SNS		Student’s  t-Test
		Mean Response	Standard Deviation	Mean Response	Standard Deviation
1.	I feel that I’m a person of worth, at least on an equal plane with others	2.80	1.44	4.17	0.72	0.0123
2.	I feel that I have a number of good qualities	3.48	1.45	3.58	1.38	0.4332
3.	I am able to do things as well as most other people	3.23	1.19	3.50	1.24	0.4432
4.	I take a positive attitude toward myself	3.85	1.05	3.92	0.79	0.1525
5.	On the whole, I am satisfied with myself	3.70	1.32	4.08	1.24	0.1826
	Total Psychological Well-being (Cronbach’s alpha = 0.73 )	17.05	4.52	19.25	4.16	0.2865

Unless otherwise stated, the response recording made from 1 (Strongly disagree) to 5 (Strongly agree)

Table 5: Social Capital

S.N.	Parameter	Active on SNS		No SNS		Student’s  t-Test
		Mean Response	Standard Deviation	Mean Response	Standard Deviation
A	Bridging Social Capital
1.	I feel I am part of my school and residing community	4.20	1.07	4.00	0.95	0.1744
2.	I am interested in what goes on at my school and residing community	3.58	0.98	3.25	1.36	0.4538
3.	I would be willing to contribute money to my Alma matter	2.63	1.41	3.00	1.76	0.1525
4.	Interacting with people makes me feel like a part of larger community	3.40	1.34	3.92	1.16	0.0121
5.	I am willing to spend time to support school and residing community	3.38	1.29	4.25	0.97	0.2891
	Total Social Capital (Cronbach’s alpha =  0.48)	17.18	3.50	18.42	4.56	0.359776
B	Bonding Social Capital
6.	There are several people at my school I trust to solve my problems	3.70	1.26	3.42	1.31	0.2891
7.	If I needed an emergency loan, I know someone at my community	2.68	1.42	2.58	1.78	0.2387
8.	There is someone at my school community I can turn to for advice	3.40	1.13	2.92	1.44	0.2050
	Total Social Capital (Cronbach’s alpha =  0.61)	9.78	2.87	8.92	3.75	0.207371

Unless otherwise stated, the response recording made from 1 (Strongly disagree) to 5 (Strongly agree)

Table 6: Prevalence of Mental Health Indicators

Mental Health Indicators	Users Active on SNS (N = 40)			Users Not Active on SNS (N = 12)
	Normal	Moderate	Severe	Normal	Moderate	Severe
Anxiety	35% (14)	15% (6)	50% (20)	16.67% (2)	16.67% (2)	66.67% (8)
PN	30% (12)	-	70% (28)	25% (3)	-	75% (9)
GD	50% (20)	-	50% (20)	50% (6)	-	75% (6)
SP	22.5% (9)	-	77.5% (31)	16.67% (2)	-	83.33% (10)
SC	72.5% (29)	-	27.5% (11)	75% (9)	-	25% (3)
SH	82.5% (33)	-	17.5% (7)	83.33% (10)	-	16.67% (2)
Depression	35% (14)	27.5% (11)	37.5% (15)	33.33% (4)	41.67% (5)	25% (3)

Anxiety values derived from SCARED (3 point Likert scale); Depression derived from CES-DC (4 point Likert scale)

Table 7: Correlational study of Mental Health Indicators

Social Networking Indicators	Anxiety		Depression
	Users Active on SNS	Users Not Active on SNS	Users Active on SNS	Users Not Active on SNS
Television Exposure (in hrs)	-0.12868	0.174742	-0.08262	0.424559
Internet Surfing (in hrs)	-0.1542	0.025093	-0.08013	0.13076
Facebook Members	-0.16339	-	-0.28723	-
Facebook Intensity	-0.41834	-	-0.33586	-
Average Calculated Intensity	-0.41147	-	-0.28867	-
Facebook Usage Pattern	-0.1258	-	-0.13252	-
Psychological Well-being	0.021154	-0.27012	-0.08832	0.218785
Bridging Social Capital	-0.19491	-0.32319	-0.07317	0.154433
Bonding Social Capital	-0.10422	0.428535	0.108469	-0.01209

Discussion:

Attribute of social networks and mental health of adolescents are blurred and plausible because of the nature and patterns in planning, data collection traits and methods of studies. There is huge diversity in pattern and selection of parameters to large extent. The study is in initial phase, availability of literature is in childhood and rigor deficit design due to un-harmonised planning outline and inadequately designed questionnaire. Even though some preliminary and established alliance between social networking site surfing and mental health are demonstrated. The planning, questionnaire and data collection method need to be harmonised and authenticated before depiction of any actual conclusion. On the other hand, it is impervious to declare the collected data redundant as previous data is important for collective responses (Toseeb and Inkster, 2015). Gender specific results are also obtained as girls are more susceptible to social media usage as high media surfing may result in low life satisfaction (Orben, Dienlin and Przybylski, 2019). Present study also conclude that majority of control subjects have high degree of mental issues despite of non-social media background. It is also noted that there are positive correlation with TV exposure with depression. Serious concerns were raised by Srygley (1978) in pre-social media era which showed influence of TV time with increasing aggression, violence and crime among young ones. The possible addiction among children was also highlighted due to high screen time. The finding of Pantic et al. (2012) is contradictory as insignificant correlation between BDI-II score and TV time were reported.

The social media might have involved in slightly improving the psychological and social well being as compared to non-users although the correlation coefficient indicated low positive for anxiety to negligible indicator for depression but trend is harmonious. This finding is in co-ordinance with Objective self awareness theory of Gonzales and Hancock (2011). McPherson et al. (2014) also concluded that social capital at family and community level exhibited highly influential role in mental health among adolescents. Pantic et al. (2012) proposed new sub-theory as objective self awareness may be induced in respect of over use of social media and early indicator of depression, which is not visualised in present study.

Present study showed the higher percentage of anxiety (50%) and depression (66.67) amonh non-users control group which is contradictory to findings of Sarda et al. (2013) in which only 11.48% mental disorders were reported. This might be due to various aspects like survey time, any stress like examination, condition and individual perception. With reference to Facebook mediated mental disorder screening Labrague (2014) concluded that anxiety, depression and stress are significantly correlated with high usage of Facebook among adolescents which might be not purely in accordance of this finding. In both study the major concern is number of users per Facebook and time spent over Facebook. In that scenario both studies exhibited same pattern.

Majority of psychological effects of social media on adolescents are inconclusive on time spent on social site irrespective of other factors like gender, emotional self efficacy, psychological well-being and life satisfaction which also contributed as intervening variables along with the prime factor as mental illness is directly determined as person to person.

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