The essay topic is about "social media Pros and Cons". It is a comparative research essay that must strictly follow the academic essay structure. Requirements: 1. 5-7 page, double spaced, MLA 2. Must

Versatile features, such as communication, messaging, gaming, navigation, access to the Internet, multimedi a, and access to social net - works (perhaps the most important part), can be considered, among the reasons for preferences.

The fact that smartphones are being used so often in our daily practice and that they are indispensable have caused us to face the con - cept of ‘smartphone addiction’. 11 Researchers have argued that everything that gives excite- ment carries the risk of add iction.

12 According to the behavioral approach, if a behavior gives happiness and/or helps to get rid of negative behavior, the person tends to this behavior and the person continues to take pleasure or escape from distress and negative behavior. 13 When we look at it from this direction, the smartphone gives pleasure to the person and/or he/she becomes to be distant from the distress and the stress, so the risk of addiction is always pr esent.

Another reason that increases the risk of addic - tion is the growing number of social media services in usage and variety. Social media networks, despite the fact that people have come out to communicate, evolved over time in differ - ent directions. T oday, there are social media networks with all kinds of contents and sharing, such as music, photos, videos, messages, shopping, and meetings. The most suitable population for using these social media networks and the more prone group of the smartphone add iction is the university students who think that they can express themselves better in the social media networks. 14 Previous studies in the literature have shown that smartphone overuse and addiction are related to sleep quality. 15,16 To the best of our knowledge, there is no study showing the rela- tionship between the social media networks used and sleep quality. W ith the hypothesis that the use of different social media networks will affect smartphone addiction and sleep quality, the aim of this study is to determine the effect of social media networks on smartphone addiction and sleep quality and examine the various related factors, in university students. METHODS Desi gn The study was conducted in the Department of Psychiatry, Health Sciences University Sultan Abdulhamid Han Training and Research Hospi - tal, İstanbul. This study is a cross -sectional survey conducted in a sample of university students dur ing the period of May and October 2017. All accessible students from two state universities in İstanbul (Health Sciences Univer - sity and Marmara University) were invited to the study. Only volunteer students were included in the study. Eighty -seven participants were ex - cluded from the survey due to their inability to complete the survey or lack thereof, and thus 1369 of 1456 university students were included. Anatolian Journal of Psychiatry 2020; 21(2):14 1-148 Gündoğmuş et al. 143 _____________________________________________________________________________________________________ The volunteers who are still a formal university student who fulfill the inclusion criteria for over a year's use of smartphones were included in the study. Exclusio n criteria were having psychiatric diagnosis and treatment or physical diseases that can alter sleep, failing in filling up the ques - tionnaires (less than 80% of questions), and not wanting to participate in the study. The ethical protocol of the study was approved by the Ethics Committee of the Haydarpaşa Numune Training and Research Hospital and informed consent was taken from all the volun- teers before filling out the questionnaires. The study complied with the Helsinki Declaration standards. The data were collected with a standardized, anonymous, self -report data collection page.

Personal information, habits, educational infor - mation, and smartphone information were asked for all attendees, along with the sociodemog- raphi c form. All volunteers were asked to ques - tion the quality of sleep, the Pittsburgh Sleep Quality Index (PSQI) self -rated scale, and the Smartphone Addiction Scale- Short Version (SAS- SV) for querying the smartphone addiction status. They were grouped into cases and control following the questionnaires. The social media networks of the volunteers were deter - mined after the statistics. 17 Measures The sleep quality in this study was assessed by the Pittsburgh Sleep Quality Index that was developed by Buysse et al.

18 The Turkish validity and reliability study was conducted by Agargun et al. 19 PSQI is a 19 -item standard self -report questionnaire that assesses sleep quality of last 1 month that yields seven component scores:

daytime dysfunction, habitual sleep efficiency, use of sleep medications, sleeps disturbances, and overal l sleep quality, duration of sleep, and sleep latency. Each component was scored between 0 and 3 and the total scale score ranged from 0 to 21. High scores are associated with poor sleep quality and also low scores with good sleep quality. According to previous studies, the PSQI global score of less than 5 was associated with better sleep quality and higher than 6 with poor sleep quality. 20 Smartphone addiction was measured by SAS - SV that is a validated scale that contains 10 items. 21 The scale consisted of 10 questions, are Likert type. (1 ‘ strongly disagree ’ to 6 ‘strongly agree ’) The scores to be obtained from the scale vary between 10 and 60. High scores are asso- ciated with smartphone addiction in the last year.

Turkish validity and reliability study Noyan et al. 22 conducted. According to Kwon et al. 21 cutoff values of ≥31 and ≥33 smartphone depen- dencies were determined for male and female participants, respectively. S tatistical analysis After data gathering and calculation of sleep quality scores and smartphone addiction score for each study subjects, the data analysis was performed using SPSS (Version 20.0, Chicago, IL) for W indows. After the descriptive analysis was used to examine the sociodemographic characteristics (gender, age, and educational term) of the study participants, numerical vari - ables were presented as mean±standard devia- tion and categorical variables were presented as frequency and percentages. To determine whether to use parametric or non- parametric tests, it was first determined whether the normal distributions were appropriate. Student’s t- test and one- way analysis of variance test were used for normal distribution variables. Tukey post -hoc analysis was used to show differences between groups. Mann- Whitney U test and Kruskal -Wallis test were used for non- normal distributional vari - ables. Bonferroni post-hoc analysis was used to show differences between groups. Chi -square tests were used to compare the differences between categorical variables. Pearson ’s and Spearman’s correlations were used to determine the relationship between variables. Statistical significance was accepted as p values less than 0.05. RESULT S Overall, 1369 university students participated in th is study and completed the questionnaires.

Socio demographic data, SAS -SV score and PSQI score of the participants are shown in Table 1. The students consisted of 57.8% fe- males and 42.2% males. The mean age of the participants was 21.54±2.97 years (range: 18-45 years). The average SAS -SV score was 31.06±10.33 and PSQI score was 5.48±3.40.

Statistically significant difference was found be- tween male and female SAS -SV scores because of the difference in cut -off scores of females and males . 21 The mean SAS -SV s core was calcu- lated based on demographic variables and statistically significant differences were found between gender, frequency of smartphone change, monthly smartphone bill, smartphone addiction, and sleep quality. ( p<0.001; p < 0.001; An adolu Psikiyatri Derg 2020; 21(2):141 -148 144 Investigation of the relationship between social network usage and sleep quality … _____________________________________________________________________________________________________ Table 1. Characteristics of the p articipants, comparisons of mean values for PSQI and SAS -SV in different groups _____________________________________________________________________________________________________ PSQI total score SAS-SV score Variable n % Mean±SD p Mean±SD p _____________________________________________________________________________________________________ All participants 1369 100.0 5.48±3.40 31.06±10.33 Gender Female 791 57.8 5.42±3.38 0.456 32.34±9.91 <0.001 Male 578 42.2 5.56±3.43 29.41±10.64 Body Mass Index Underweight 124 9.1 5.37±3.36 0.417 31.87±10.40 0.223 Healthy 961 70.2 5.44±3.34 31.09±10.24 Overweight 234 17.1 5.55±3.60 30.12±10.77 Obese 50 3.7 6.24±3.57 32.96±9.61 Faculty Medicine 185 13.5 4.84±3.22 0.90 30.83±9.91 0.800 Law 72 5.3 5.70±3.72 31.05±10.14 Education 88 6.4 5.68±3.37 29.86±10.18 Economics 203 14.8 5.71±3.69 31.26±11.31 Engineering 314 22.9 5.37±3.18 29.72±10.08 Healthy Sciences 164 12.0 5.87±3.47 31.59±10.37 Junior Technical College 106 7.7 5.87±3.69 32.00±10.50 Place of residence Family 546 39.9 5.40±3.43 0.91 31.19±10.42 0.804 Friend 303 22.1 5.90±3.54 30.55±10.61 Alon e 129 9.4 5.51±3.40 31.14±10.18 Student hostel 391 28.6 5.27±3.31 32.26±10.04 Monthly income Low 320 23.4 5.95±3.60 0.12 31.30±11.50 0.338 Middle 601 43.9 5.43±3.28 31.37±10.10 High 448 32.7 5.22±3.38 30.47±9.72 Frequency of smartphone change 0 -1 Years 35 2.6 6.91±4.59 <0.001 36.45±11.76 <0.001 1 -2 Years 293 21.4 6.09±3.65 33.67±10.80 2 -4 Years 730 53.3 5.31±3.20 30.93±9.88 4 and more 311 22.7 5.15±3.36 28.32±9.93 Monthly smartphone bill Very low 139 10.2 5.28±3.21 0.005 29.28±10.17 0.001 Low 789 57.6 5.28±3.31 30.73±10.30 Middle 290 21.2 5.72±3.48 31.30±9.99 High 151 11.0 6.27±3.78 34.00±10.77 Smartphone addiction Yes 739 54.0 6.24±3.52 <0.001 40.10±6.39 <0.001 No 630 46.0 4.84±3.16 23.36±5.81 Sleep quality Good 787 57.5 3.08±1.33 <0.001 24.48±9.42 <0.001 Poor 582 42.5 8.73±2.54 34.55±10.48 _____________________________________________________________________________________________________ PSQI: Pittsburgh Sleep Quality Index ; SAS -SV : Smartphone Addictions Scale Short Version. p=0.001; p< 0.001; p<0.001, respectively) The mean PSQI score was calculated based on demographic variables and statistically signifi- cant differences were found between frequency of smartphone change, monthly smartphone bill, smartphone addiction, and sleep quality ( p <0.001; p =0.005; p<0.001; p<0.001, respec- tively) (Table 1). It was statistically significant when students' daily Facebook, Twitter, Instagram, Snapchat, Swarm, and Foursquare use times (non- user group, 1- 60 minute/day, 61- 120 minute/day, 121 and over minute/day) were compared with the mean score of SAS -SV but it was statistically insignificant when were compared the mean score of SAS -SV of student who using LinkedIn and Pinterest. ( p<0.001; p<0.001; p<0.001; p <0.001; p<0.001; p<0.001; p =0.008; p=0.088; Anatolian Journal of Psychiatry 2020; 21( 2):14 1-148 Gündoğmuş et al. 145 _____________________________________________________________________________________________________ Table 2. Comparisons of SAS -SV scores with daily usage of social media networks ___________________________________________________________________________________________________ Smartphone Addictions Scale Short Version s core Non -user g roup 1-60 61-120 min/day min/day min/day 121 and over Mean±SD/ Mean±SD/ Mean±SD/ Mean±SD/ n % n % n % n % p ___________________________________________________________________________________________________ Facebook 28.67±9.43 a 30.69±9.93 b 33.17±10.25 c 36.68±12.08 d <0.001 374 27.3 681 49.7 176 12.9 138 10.1 Twitter 28.20±9.38 a 32.41±9.66 b 34.08±11.08 b 37.90±12.42 c <0.001 619 45.2 485 35.4 181 13.2 84 6.1 Instagram 25.91±9.65 a 29.49±9.33 b 33.33±9.48 c 36.69±12.19 d <0.001 202 14.8 595 41.3 433 31.6 169 12.3 Snapchat 28.55±9.65 a 31.93±9.80 b 38.50±11.86 c 39.44±13.10 c <0.001 565 41.3 700 51.1 59 4.3 45 3.3 LinkedIn 31.31±10.39 a 29.30±10.22 a 32.54±2.84 a 33.28±3.35 a 0.088 1169 85.4 182 13.3 11 0.8 7 0.5 Swarm 29.80±10.17 a 33.24±10.05 b 37.47±13.14 b 35.45±10.39 b <0.001 897 65.5 442 32.35 19 1.4 11 0.8 Pinterest 31.08±10.49 a 30.77±9.20 a 34.33±9.48 a 30.42±8.40 a 0.787 1196 87.4 157 11.5 9 0.7 7 0.5 Foursquare 30.74±10.25 a 33.42±1097 b 36.57±2.50 37.33±2.08 0.008 1217 88.9 142 10.4 7 0.5 3 0.2 ___________________________________________________________________________________________________ a,b,c,d : the same letters are not statistically different, the different letters are statistically different. Table 3. Comparisons of PSQI Scores with daily usage of social media networks ___________________________________________________________________________________________________ Pittsburgh Sleep Quality Index score Non -user g roup 1-60 61-120 min/day min/day min/day 121 and over M ean±SD Mean±SD Mean±SD Mean±SD n % n % n % n % p ___________________________________________________________________________________________________ Facebook 4.94±3.03 a 5.31±3.28 a 6.12±3.41 b 6.94±4.30 b <0.001 374 27.3 681 49.7 176 12.9 138 10.1 Twitter 4.91±3.09 a 5.62±3.06 b 6.17±4.06 b 7.48±4.62 c <0.001 619 45.2 485 35.4 181 13.2 84 6.1 Instagram 4.80±2.87 a 5.15±3.32 a 5.73±3.32 b 6.79±4.01 c <0.001 202 14.8 595 41.3 433 31.6 169 12.3 Snapchat 5.28±3.22 a 5.40±3.36 a 6.50±4.35 b 7.95±3.71 b <0.001 565 41.3 700 51.1 59 4.3 45 3.3 LinkedIn 5.53±3.39 a 5.25±3.60 a 4.63±1.12 a 5.71±3.40 a 0.622 1169 85.4 182 13.3 11 0.8 7 0.5 Swarm 5.31±3.32 a 5.74±3.42 a 7.05±5.32 a 6.72±3.58 a 0.016 897 65.5 442 32.4 19 1.4 11 0.8 Pinterest 5.48±3.38 a 5.61±3.65 a 4.88±1.96 a 4.57±2.96 a 0.802 1196 87.4 157 11.5 9 0.7 7 0.5 Foursquare 5.50±3.39 a 5.28±3.42 a 6.42±3.55 a 7.33±4.50 a 0.582 1217 88.9 142 10.4 7 0.5 3 0.2 ___________________________________________________________________________________________________ a,b,c,d : the same letters are not statistically different, the different letters are statistically different. p=0.787, respectively) ( Table 2). It was statistically significant when students' daily Facebook, Twitter, Instagram, Snapchat, and Swarm use times (non- user group, 1-60 minute/day, 61- 120 minute/day, 121 and over minute/day) were compared with the mean score of PSQI but it was statistically insignificant when were compared the mean score of SAS- SV of Anadolu Psikiyatri Derg 2020; 21( 2):141 -148 146 Investigation of the relationship between social network usage and sleep quality … _____________________________________________________________________________________________________ student who were using LinkedIn Pinterest, and Foursquare (p<0.001; p<0.001; p<0.001; p <0.001; p =0.016; p=0.622; p=0.802; p=0.582, respectively (Table 3). The Pearson ’s correlation showed that the smartphone addiction had a significant relation - ship with the quality of sleep ( p<0.001; r=0.326) DISCUSSION The most important finding of our study is that the increase in the time spent on daily social media has increased the risk of poor sleep quality and smartphone addiction. According to the social media kind usage, the changes of risk are another important finding of the study . It has also been demonstrated that smartphone addic - tion and the use of various social media net - works have an impact on sleep quality. The effect of the usage periods of social media net - works on smartphone dependency was investi - gated. To the best of our knowledge, this is the first study to examine the impact of various social media networks on sleep quality and smart - phone addiction. According to the results of our study, the most popular social media tool among university students is Instagram, secondly Facebook, and respectively, Twitter and Snapchat. Although it is reported that Facebook is used most in the general population, we have shown that Instag- ram is the most used in the university student population. 17 We think that the reason for this situation is that there are online sharing areas, such as photography, writing, games, shopping in these social media networks, and they can be taken against these shares. It may also make it easier to spend more time with these social media tools being more widely used and their content being richer. Furthermore, in accord- ance with the literature, the current study found that those in daily who use smartphones more have more frequent smartphone changes and that monthly smartphone bill s are higher. 23 W e think that this is due to the rapid development of smartphone technology, the speed of pro- cessing, and especially the increase of photo quality. 23 Because with the increase in quality, costs related to the use of smart phones such as internet quantity increases . Especially, among young people, it has become a necessity to more frequently change the smartphone and pay more bills, in order to get more ‘follow’, more ‘like,’ and thus more attention. 24 The results of our article support the results found in the literature. 24,25 According to Griffiths, people may develop dependence on certain activities that they have performed, even though the risk of potential dependence on the devices containing the inter - net. 26 Nowadays, social media networks can be considered as the best example of this. The increasing popularity of social media networks, the change of t he popular social media network, the change of people's social media expecta- tions, and the use of multiple social media net - works by people have led us to the concept of smartphone addiction from social media network addiction. 27 As a whole, smartphone features, such as navigation, messaging, calling, and photography, can be also significantly affecting addiction although social media networks are an important part of smartphone dependency. The results of our study also support this idea, espe- cially the usage of Instagram, Facebook, Twitter, and Snapchat increases the risk of smartphone addiction. Swarm and Foursquare have a partial impact on smartphone dependency, meaning there is a statistically significant difference be- tween those who have an account and those who do not. We think there are not appropriate applications to spend a lot of time because these two social media networks are mostly used to share the locati on. Pinterest and LinkedIn do not affect the risk of smartphone dependency ac - cording to our results. This may be because it is not so popular among university students. Our results support the information contained in the literature and contribute to the literature due to the size of the sample size. This may be because it is not so popular among university students.

Our results support the information contained in the literature, and contribute to the literature due to the size of t he sample size. 25,27 Sleep quality is an important public health prob- lem that is getting worse day by day. It is not possible to predict exactly the reason because there are too many factors that can affect sleep quality. Although there are many contradictory results in the previous studies in the literature, the results of our study suggest that social net - wor king networks are going overused and smart - phone addiction is the reason for the poor sleep quality among university students.

15,28 The in- consis tency of previous study results may be due to excessive phone use causing anxiety and depression or being used as a means of pre- venting these disorders symptoms. 29-31 Also, if the phone is used late at night, it may decrease sleep quality. 31 In addition to the literature, ac - cording to the results of the present study , using Facebook, Twitter, Instagram, Snapchat , and Anatolian Journal of Psychiatry 2020; 21( 2):14 1-148 Gündoğmuş et al. 147 _____________________________________________________________________________________________________ Swarm have been shown to have a negative impact on sleep quality. Besides the mental fatigue caused by the excessive use of social media because of this negative effect, it can be considered that the wealth of sharing such as photos, video, games, shopping, messages, especially in Facebook and Instagram, and the high expectation of these shares affected the sleep quality. Furthermore, there is no effect on LinkedIn, Pinterest and Foursquare sleep qua- lity. Social media networks have been established to communicate with friends, create personal profiles and create common groups, over time, shopping, gambling, gaming, sharing, and por - nographic content and etc. were added to their theme. But, it began to create a threat of addic - tion, with u sage out of its intended use and with much more use than was anticipated. The over - use of social media has strengthened the idea of addiction by encountering consequences like behavioral addiction, such as loss of control, withdrawal, increased use, tolera nce, extended recovery periods, relapse, sacrificing social, occupational and recreational activities, and continued use despite negative consequences.

As we mentioned in the Introduction, social media networks can become addictive because of the giving ex citement and happiness of the person using it and/or the removal from the anxiety. 12 ,13 In addition, the use of social media networks can affect human emotions and cause rapid changes in emotions. These emotions cause excitement in the person and lead to virtual happiness and goodness. In other words, the person creates a virtual world in which he/she wants to be different, happy, excited, and imagined from the real world and can use that this virtual world as a means of departing from the real world. This leads to the more frequent use of social media networks and increased risk of addiction. The results of this study should be interpreted in the light of study limitations, although there is a large sample size. First, the population in which the study is done is university students and may not reflect all the population. It should be taken into consideration that data may not be taken at the same time by the students. Furthermore, the scales used are self -report. Other limitation of our study is that the collected data are cross - s ectional and limit causal inferences. Especially, when examining sleep quality, it is always to be considered that the mixing factors are too great.

Finally, the literature is not mature enough on these issues. CONCLUSION As a result, our study showed that overuse of smartphones along with social media networks in college students is potentially addictive and will affect sleep quality negatively. It was also discussed which popular social media networks increased smartphone addiction risk, and af - fected sleep quality. To the best of our know - ledge, these results are limited in the literature and limited in number, so our study will provide significant contributions. In conclusion, it will be useful to see the overuse of social media networks as a public health problem, to avoid dependency and to use it as intended. It should be read in mind that proper use of an object for any purpose may be of great benefit, but excluding it may lead to unintended consequences. We must take the necessary precautions, es pecially among our young peo- ple, to use them appropriately for the purpose of social media networks. Future research on this topic should be added to the literature in a well - structured and planned way of doing personality analysis, social interaction and more specific topics. Authors’ contributions: İ.G.: planning, literature, data collection, statistics; A.T.K.: finding topic, literature, data collection, writing manuscript ; D.A.Ç: data collection, planning, literature, examination sample, writing manuscript. REFERENCES 1. Krystal AD. Sleep and psychiatric disorders: future directions. Psychiatric Clinics 2006; 29:1115- 1130. 2 . Bryant PA, Trinder J, Curtis N. Sick and tired: does sleep have a vital role in the immune system?

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31. Machell KA, Goodman FR, Kashdan TB. Experi - ential avoidance and well -being: A daily diary analysis. Cogn Emot 2015; 29:351-359. Anatolian Journal of Psychiatry 2020; 21( 2):14 1-148 Copyright ofAnatolian JournalofPsychiatry /Anadolu Psikiyatri Dergisiisthe property of Anatolian JournalofPsychiatry anditscontent maynotbecopied oremailed tomultiple sites or posted toalistserv without thecopyright holder'sexpresswrittenpermission. However, users mayprint, download, oremail articles forindividual use.