The Relationship Between Social Media Addiction, Happiness, and Life Satisfaction in Adults: Analysis with Machine Learning Approach

Abstract

This study was conducted to determine the relationship between social media addiction, happiness, and life satisfaction in adults. This descriptive and cross-sectional study was conducted on 15/03/2022–30/12/2022 with 1008 adult individuals in Turkey. “Personal Information Form,” “Bergen Social Media Addiction Scale,” “Oxford Happiness Scale Short Form,” and “Satisfaction with Life Scale” were used to collect data. Data were analyzed using SPSS 22.0, AMOS V 24.0, G*Power 3.1, R programming language 4.1.3 programs. As the level of social media addiction increases, life satisfaction (β=−0.235, p<0.05) and happiness (β=−0.290, p<0.05) levels decrease. It was found that life satisfaction had a mediating role in the effect of social media addiction on happiness (β=−0.139, 95% confidence interval=−0.186/−0.096). It was determined the structural equation model. The best performing algorithm for predicting the happiness variable was elastic net regression. When the contributions of the variables to the model are calculated with Shapley values (Shapley Additive Explanations (SHAP)), the most important variables that should be in the model to predict the happiness variable are life satisfaction and social media addiction variables. As the level of social media addiction increases, life satisfaction and happiness levels decrease. Longitudinal studies on social media addiction are recommended.

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Vol.:(0123456789)
International Journal of Mental Health and Addiction
https://doi.org/10.1007/s11469-023-01118-7
1 3
ORIGINAL ARTICLE
The Relationship Between Social Media Addiction,
Happiness, andLife Satisfaction inAdults: Analysis
withMachine Learning Approach
NecmettinÇiftci1 · MetinYıldız2
Accepted: 12 July 2023
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract
This study was conducted to determine the relationship between social media addiction,
happiness, and life satisfaction in adults. This descriptive and cross-sectional study was
conducted on 15/03/2022–30/12/2022 with 1008 adult individuals in Turkey. “Personal
Information Form,” “Bergen Social Media Addiction Scale,” “Oxford Happiness Scale
Short Form,” and “Satisfaction with Life Scale” were used to collect data. Data were
analyzed using SPSS 22.0, AMOS V 24.0, G*Power 3.1, R programming language 4.1.3
programs. As the level of social media addiction increases, life satisfaction (β=−0.235,
p<0.05) and happiness (β=−0.290, p<0.05) levels decrease. It was found that life satisfac-
tion had a mediating role in the effect of social media addiction on happiness (β=−0.139,
95% confidence interval=−0.186/−0.096). It was determined the structural equation
model. The best performing algorithm for predicting the happiness variable was elastic
net regression. When the contributions of the variables to the model are calculated with
Shapley values (Shapley Additive Explanations (SHAP)), the most important variables that
should be in the model to predict the happiness variable are life satisfaction and social
media addiction variables. As the level of social media addiction increases, life satisfac-
tion and happiness levels decrease. Longitudinal studies on social media addiction are
recommended.
Keywords Social media addiction· Happiness· Life satisfaction· Adults· Machine
learning
With the introduction of internet technology to people’s cell phones, the internet has
become widely used in the world. This situation increases the use of social media daily
(Halverson etal., 2016). Social media is a tool that allows individuals to create, share, and
search content (Kim, 2017; Smock etal., 2011), as well as communicate and collaborate
with each other (Pempek etal., 2009). The time spent on the internet on a daily basis is
* Metin Yıldız
yildizz.metin@gmail.com
1 Faculty ofHealth Sciences, Department ofNursing, Muş Alparslan University, 49100Muş, Turkey
2 Faculty ofHealth Sciences, Department ofMidwifery, Sakarya University, 54100Sakarya, Turkey

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a determining factor in social media addiction. The increase in this time is an indication
that social media addiction has increased and some deterioration in life functionality has
occurred with the weakening of the individual’s relations with their social environment
(Çiftçi, 2018). Ünal and Deniz describe social media addiction as a psychological problem
that affects the individual cognitively, emotionally, and behaviorally, which leads to rep-
etition in conflicts and problems in the individual’s ability to deal with life areas such as
private, work, social, and emotional situations (Tutgun-Ünal & Deniz, 2016). Social media
addiction also leads to emotional exhaustion by spending emotional energy (Zivnuska
etal., 2019).
Happiness is defined as a person’s positive emotions outweighing their negative emo-
tions. In other words, happiness is the set of feelings that a person expresses as good
(Doğan, 2013; Eryılmaz, 2016). In general, happiness is defined as a biological and psy-
chological state that every person strives to achieve throughout her life, which causes sat-
isfaction when she reaches material and spiritual satisfaction, and creates human relations
(Aluş & Selçukkaya, 2015). In some studies, it has been determined that the level of hap-
piness decreases as social media addiction increases in individuals (Kutlu etal., 2016; Pitt-
man & Reich, 2016).
Another variable associated with social media use is life satisfaction. Life satisfaction is
a concept based on individual evaluation that clarifies the positive judgments and satisfac-
tion levels of individuals about their life experiences and conditions (Diener etal., 1985).
Life satisfaction is also defined as the cognitive expression of human happiness (Diener
etal., 1999). In the literature, it is seen that there is a negative relationship between social
media addiction and life satisfaction (Ayyıldız & Şahin, 2022; Kutlu etal., 2016; Long-
street & Brooks, 2017; Sun & Zhang, 2021). People can use social media to avoid negative
moods and dissatisfaction with their lives. In other words, excessive use of social media to
manage emotions can be seen as a strategy (Kumpasoğlu etal., 2021). It has been deter-
mined that as social media addiction increases, life satisfaction decreases in individuals
(Pittman & Reich, 2016).
There are many approaches to life satisfaction, which is one of the components of hap-
piness (Çevik & Korkmaz, 2014). Some of these approaches are bottom-up-top-down
approach (Kozma & Stones, 1980), purposive approach (Çevik & Korkmaz, 2014), and
activity approach (Çevik & Korkmaz, 2014). The bottom-up-top-down approach is a very
popular one in contemporary psychology. According to the bottom-up approach, the indi-
vidual determines the happiness in his life by evaluating the periods when he is happy for
himself and when he is not. According to this approach, the combination of happy moments
creates a happy life (Kozma & Stones, 1980). According to the top-down approach, when
the individual is happy, she gets more satisfaction from her own life (Kozma & Stones,
1980). In the activity approach, it is argued that the happiness of the individual stems from
his own activities. According to the purposive approach, satisfied needs lead to happi-
ness and unsatisfied needs lead to unhappiness (Çevik & Korkmaz, 2014). Longstreet and
Brooks stated in their study that a higher level of happiness corresponds to a higher life
satisfaction (Longstreet & Brooks, 2017).
Some studies associate individuals’ social media addiction with life satisfaction and happi-
ness (Ansari etal., 2016; Li etal., 2015). However, since studies in which the characteristics
of adults in terms of these three concepts are determined and the relationship between them
is revealed are limited, it is thought that this study will contribute to the literature and support
other studies to be conducted. This one was done to determine the relationship between social
media addiction, happiness, and life satisfaction in adults. With this study, the effect of social
media addiction and life satisfaction on the happiness level of individuals has been revealed,

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and it has also been supported by the structural equation model, which is one of the important
models in the literature, and the machine learning approach, which is an up-to-date approach.
Hypotheses of the study:
H1: There is a significant relationship between social media addiction and life
satisfaction.
H2: There is a significant relationship between social media addiction and happiness.
H3: There is a significant relationship between life satisfaction and happiness.
H4: Life satisfaction has a mediating role in the effect of social media addiction on
happiness.
Methods
In this study, the quantitative-cross-sectional-descriptive survey design method was used.
It was conducted with 1008 individuals between 15/03/2022 and 30/12/2022 and done to
determine the relationship between social media addiction, happiness, and life satisfaction
in adults.
Population andSample oftheStudy
The population of the study consists of individuals aged 18 and over living in Turkey.
The minimum number of individuals to be included in the sample was calculated as 384
using the formula of unknown population (n=t 2.p.q/d 2) with a 95% confidence interval
(d=0.05), t=1.96, p=0.5, and q=0.5. In our study, 1008 individuals were included. In the
post hoc power analysis conducted in line with the results obtained from 1008 participants,
the power of our study was calculated to be 99% at a 95% confidence level with a medium
effect size (Cohen, 1988). STROBE guidelines were used in reporting this research article
(Vandenbrouckel etal., 2007).
Inclusion Criteria
All individuals who agreed to participate in the study were 18 years or older, did not have
a disease that would prevent them from completing the questionnaire, and volunteered to
participate in it.
Exclusion Criteria
Individuals who refused to participate in the study and those who left the study data incom-
plete or did not complete the questionnaire and scale questions completely were excluded
from the study.
Type ofStudy
Data Collection Tools
“Personal Information Form,” “Bergen Social Media Addiction Scale,” “Oxford Happiness
Scale Short Form,” and “Satisfaction with Life Scale” were used as data collection tools.

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Personal Information Form
The personal information form prepared by the researchers consisted of 6 questions
(age, gender, family type, mother’s education level, father’s education level, and monthly
income).
Bergen Social Media Addiction Scale
The Bergen Social Media Addiction Scale developed by Andreassen and colleagues
(Andreassen et al.) consists of six items (Andreassen et al., 2016). Its Turkish language
adaptation was conducted by Demirci (2019). Each item in the scale meets six basic addic-
tion criteria: mental occupation, mood change, tolerance, withdrawal, conflict, and failed
quit attempts. The scale is answered on a 5-point Likert-type scale ranging from (1) very
rarely to (5) very often. The score obtainable from the scale varies between 6 and 30. There
are no reverse items in it. An increase in the score on the scale indicates an increase in social
media addiction. The internal consistency of the scale was found to be 0.83 (Demirci, 2019).
In our study, Cronbach’s alpha value came out as 0.80. Social media addiction scale was
included as an independent variable in the study, and its construct validity was tested with
confirmatory factor analysis. According to the results of the analysis, the fit indices were
determined as x²/df =2.966, RMSEA=0.044, CFI=0.99, GFI=0.99, AGFI=0.97, IFI=0.99,
and TLI=0.98 and the structure of the scale was confirmed (Karagöz, 2019).
Oxford Happiness Scale Short Form (OHS‑SF)
The adaptation of the OHS-SF developed by Hills and Argyle (2002) was conducted by
Doğan and Akıncı Çötok (2011). The OHS-SF consists of 7 items and is a 5-point Likert-
type scale. Items 1 and 7 are reverse-coded. High scores obtained from the scale indicate
that happiness level scores are high. The scale consists of one dimension. The internal con-
sistency coefficient of the scale was 0.74 and the test-retest reliability coefficient was 0.88.
A high score on the scale indicates that the happiness level of the individual is high. In
our study, Cronbach’s alpha value came out as 0.72. Oxford happiness scale was included
as the dependent variable in the study. The construct validity of the scale was tested by
confirmatory factor analysis. According to the results of the analysis, the fit indices were
determined as x²/df=4.247, RMSEA=0.05, CFI=0.96, GFI=0.98, AGFI=0.96, IFI=0.96,
and TLI=0.96 and the structure of the scale was confirmed (Karagöz, 2019).
Satisfaction withLife Scale
The Satisfaction with Life Scale (SWLS) was developed by Diener etal. to determine the life
satisfaction levels of individuals (Diener etal., 1985). The scale was adapted to Turkish lan-
guage by Dağlı and Baysal (2016). The life satisfaction scale is a one-dimensional and 5-item
5-point Likert-type measurement tool. The scoring of the statements in the scale is as follows:
I do not agree at all (1), I agree very little (2), I agree moderately (3), I agree to a great extent
(4), and I completely agree (5). Cronbach alpha coefficient of the scale adapted to Turkish
language is 0.88. In our study, Cronbach’s alpha value was 0.82. Satisfaction with Life Scale
was included as the dependent variable in the study, and its construct validity was tested by

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confirmatory factor analysis. According to the results of the analysis, the fit indices were
determined as x²/df=3.134, RMSEA=0.04, CFI=0.99, GFI=0.99, AGFI=0.98, IFI=0.98, and
TLI=0.98 and the structure of the scale was confirmed (Karagöz, 2019).
Data Collection
A total of 1008 individuals participated in the study. The information obtained from the
participants per the principles of data confidentiality was taken by gaining informed con-
sent from each participant. Participants were asked to approve this form before starting the
study.
Data Evaluation
The analysis of the research data was performed using SPSS 22.0, AMOS V 24.0,
G*Power 3.1 statistical package programs. The significance level (p) was taken as 0.05
for statistical tests. The tests used in the SEM model evaluation of the data are given in
Table1. Analyses for the estimation of the happiness variable were performed with the R
programming language version 4.1.3. While performing the analyses, ggplot2, hrbrthemes,
hexbin, and GGally packages were used for graphics; SHAPforxgboost and xgboost pack-
ages were used for shap graphics. In order to apply and compare machine learning meth-
ods, 10-fold cross validation method was applied with caret package. In the caret package
used for the cross validation method, knn for K nearest neighbor regression (KNN), svm-
Radial for support vector machine regression (SVM), avNNet for artificial neural network
Table 1 Statistical methods used in data analysis
Features evaluated Statistical methods
Determining the conformity of the data to normal
distribution
• Skewness coefficient
• Coefficient of kurtosis
Determination of descriptive characteristics • Percentage distribution
• Frequency distribution
Determining the relationships between variables
and creating a model
• Structural equation modeling (maximum likelihood
estimation)
Evaluation of model fit • Fit indices
- Adjusted Chi-Square Statistic (Χ2/Sd)
- Fit Index (GFI)
- Adjusted Fit Index (AGFI)
- Comparative Fit Index (CFI)
- Root Mean Square Error of Approximation
(RMSEA)
- Incremental Fit Index (IFI)
Model assumption analysis • Multiple normal distribution
- Skewness value
- Kurtosis value
- Distance Mahalonobis
• Multiple linear connection
- Tolerance value
- Variance growth factor (VIF)
Ensuring the validity of measurement tools Confirmatory Factor Analysis (Fit indices)
Ensuring the reliability of measurement tools Cronbach’s alpha coefficient

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regression (ANN), rf for random forest (RF), xgbLinear for XGBoost, rpart for Decision
Tree regression (CART), and glmnet for Regression (REG) functions were used to deter-
mine the best performance of the methods. Alpha value between (0,1) represents elastic net
mixing parameter values. If Alpha is 0, it is Ridge regression and if Alpha is 1, it is Lasso
regression. Lambda is the strength of the penalty on the coefficients.
Structural Equation Modeling (SEM)
In this section, a structural equation model was constructed and tested to determine the
relationship between social media addiction, the independent variable of the study, and life
satisfaction and happiness, the dependent variable of the study.
Assumption Analyses
SEM is a multivariate statistical method that offers the opportunity to test multiple rela-
tionships at the same time and calculates the causality relationship between variables by
modeling (Collier, 2020; Gürbüz, 2019). There are pre-assumption tests for SEM analysis.
These are:
• Adequacy of sample size
• Variables having multiple normal distribution
• Lack of multicollinearity between variables
• Absence of outliers
When the assumptions were examined, it was determined that the study was in the over
200 sample class (Collier, 2020), which is considered a large sample volume for struc-
tural equation modeling with a sample size of 1008. In looking at the multiple normal
distribution of the variables, kurtosis and skewness values were examined. For multiple
normal distribution, the skewness value should be between −2 and +2, and the kurtosis
value should be between −10 and +10 (Collier, 2020). The skewness value was found to
be in the range of −0.341 to 0.001; the kurtosis value was found to be in the range of
−0.565/−0.111; and multiple normal distribution was provided for the variables. Many
parameters are examined for multicollinearity between variables. In the field of nursing,
tolerance and variance inflation factor (VIF) are among the values examined (Lee & Lee,
2022; Mottaghi etal., 2019; Yoon etal., 2021). In the study, a tolerance of 1.00–0.940
(>0.10) and a VIF value of 1.000–1.063 (<10) were determined for the dependent and
independent variables. According to these value ranges, the conclusion was that there was
no multicollinearity between the dependent and independent variables. While determining
outliers, Mahalanobis distance and p1/p2 values were examined, and no outlier was found.
Reliability Analyses oftheScales
Before testing the SEM model, the reliability of the variables gets tested. In the study, the
reliability of the variables was tested by determining Cronbach’s alpha coefficient (>0.60)
(Hu & Bentler, 1999; Karagöz, 2019) values of the scales. It was determined that Cron-
bach’s alpha coefficients of the measurement tools were between 0.72 and 0.82.

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Ethical Principles oftheStudy
Approval for the research was obtained from the Scientific Research and Publication Eth-
ics Committee of a university (Date and Number: 01.03.2022-42263). The individuals who
would participate in the study were informed face-to-face about the purpose of the study,
the method, the time they would allocate for the study, that participation would not cause
any harm, and that participation was completely voluntary, and their consent was obtained.
The Helsinki Declaration on Human Rights was adhered to throughout the study to protect
individual rights.
Limitations andGeneralizability oftheStudy
This study can only be generalized to the group in which the research was conducted. In
the study, the order of the scales and the situation in which the data were collected may be
due to method bias.
Results
It was found that 68.1% of the individuals who participated in the study were female, 71.7%
lived in nuclear families, 38.4% had illiterate mothers, 36.7% had literate fathers, 68.8%
had incomes equal to their expenses, and the mean age of the individuals was 35.01±12.12
(years) (Table2).
Following the assumption analyses and the determination that the measurement tools
were valid and reliable, a structural equation model was established to determine the rela-
tionship between the scales (Figs.1 and 2). It was determined that the model created in
accordance with the hypotheses was compatible, and the model fit indices were within
the desired limits as x²/df=3.480, RMSEA=0.05, CFI=0.93, GFI=0.95, AGFI=0.93, and
IFI=0.93 (Karagöz, 2019) (Table3).
Model results:
H1: There is a significant relationship between social media addiction and life satisfac-
tion. The hypothesis was confirmed (p<0.05), and hypothesis H1 was accepted (Fig.3 and
Table4). As the level of social media addiction increases, life satisfaction decreases.
H2: There is a significant relationship between social media addiction and happiness.
and H2 hypothesis was accepted (Fig.3 and Table4). As the level of social media addiction
increases, the level of happiness decreases.
H3: There is a significant relationship between life satisfaction and happiness. The
hypothesis was confirmed (p<0.05), and hypothesis H3 was accepted (Fig.3 and Table4).
As life satisfaction increases, happiness level increases.
H4: Life satisfaction has a mediating role in the effect of social media addiction on hap-
piness. The hypothesis was confirmed (p<0.05), and hypothesis H4 was accepted (Fig.3
and Table4).
Age, gender, family type, monthly ıncome, mother education, father education, social
media addiction, and life satisfaction variables were used for the estimation of happiness
variable. In the prediction model, the most accurate parameter value was determined for 7
algorithms by applying 10-fold cross validation. In order to find the most accurate param-
eter value, the data set was divided into 70% train and 30% test data and the methods were

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Table 2 Descriptive characteristics of individuals (n=1008)
Demographic characteristics n%
Gender Male 322 31.9
Female 686 68.1
Family type Nuclear family 723 71.7
Extended family 285 28.3
Mother’s education level Illiterate 387 38.4
Literate 140 13.9
Primary Education 321 31.8
Secondary Education 114 11.3
Higher Education 46 4.6
Father’s education level Illiterate 85 8.0
Literate 81 36.7
Primary Education 370 36.3
Secondary Education 366 10.5
Higher Education 106 8.4
Monthly income status Income less than expenses 299 29.7
Income equal to expenses 693 68.8
Income higher than expenses 16 1.6
X
±SD (Min-Max)
Age (years) 35.01±12.12 (19–69)
Path a
Social Media
Addiction (X)
Oxford
Happiness (Y)
Fig. 1 Simple effect model
Path b Path c
Path d
Social Media
Addiction (X)
Life
Satisfaction (M)
Oxford
Happiness (Y)
Fig. 2 Structural equation model predicted between social media addiction, life satisfaction, and happiness

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compared. There are 708 observations in train data and 300 observations in test data. In
order to determine the most accurate parameter value of the algorithms, train data was esti-
mated and shown in Fig.4.
Figure5 shows the comparison metrics (RMSE, MAE) for the best parameter values.
Figure2 shows the RMSE and MAE values obtained as a result of estimating the most
accurate metric values obtained with the train data with the test data. When the metric val-
ues are analyzed, although every model except the REG model produces close and success-
ful predictions, the REG model gives the most accurate result. Since the alpha parameter
value that gives the most accurate result in the REG model is 0.6, elastic net regression
Table 3 Fit Index values of the
model Fit Index Research model Normal value Acceptable value
χ2/sd 3.480 <2 <5
GFI 0.95 >0.95 >0.90
AGFI 0.93 >0.95 >0.85
IFI 0.93 >0.95 >0.90
CFI 0.93 >0.95 >0.90
RMSEA 0.05 <0.05 <0.08
Fig. 3 SEM diagram showing the relationship between social media addiction, life satisfaction, and happi-
ness

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was used (Fig.2). R^2 values of the methods: ANN 0.4382699, CART 0.3698576, KNN
0.4179933, RF 0.3969341, SVM 0.4184988, XGBoost 0.2974995, and REG 0.4405418.
A prediction table can be created for the REG method. In Fig.6, we can see the test data
predicted by the REG model with red lines. The blue lines are the actual test data. Visually,
we can say that the closer the red lines are to the blue lines, the more successful the predic-
tion model is.
In order to predict the happiness variable, all variables were compared with the perfor-
mance of machine learning algorithms. It was then found that the best performing algo-
rithm was elastic net regression. The contributions of these variables to the model were
calculated with Shapley values (Shapley Additive Explanations (SHAP)). The SHAP val-
ues of the variables in the best performing model were examined to avoid bias in terms
of comparison in the performance criterion. SHAP (Shapley Additive Explanations) val-
ues show the contribution or importance of each variable in the estimation of the model.
As can be seen in the graph, the most important variables that should be in the model
to predict the happiness variable are life satisfaction and social media addiction variables
(Fig.7).
Discussion
In our study, we aimed to determine the effect of social media addiction on happiness and
life satisfaction in adults. In this section, the findings are discussed in light of the literature.
In our study, there is a significant relationship between social media addiction and life
satisfaction. As the level of social media addiction increases, life satisfaction decreases.
The result of this study supports the conclusion of Longstreet and Brooks (2017) that
social media addiction has significant effects on life satisfaction and that social media
addiction is a factor that reduces individuals’ life satisfaction. In the study conducted by
Brailovskaia etal. (2021), the result of this study also supports the conclusion that there
Table 4 The relationship between social media addiction, life satisfaction, and happiness of individuals
Result variables
Life satisfaction Happiness
ΒS.H. βS.H.
Social media addiction (path a) -0.290 0.033
R20.178
Social media addiction (path b) −0.235 0.032
R20.083
Life satisfaction (path c) 0.591 0.051
Social media addiction (path d) −0124 0.022
R20.697
Effects β95% confidence interval (Lower
bound–upper bound)
Total effect −0.263 −0.343/−0.185
Direct effect −0124 −0.179/−0.073
Indirect effect −0.139 −0.186/−0.096

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Fig. 4 KNN, SVM, ANN, RF, XGBoost, CART, and REG algorithms models used for the estimation of
happiness variable

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is an inverse relationship between social media addiction and life satisfaction. In addi-
tion, in the study conducted by Boer etal. (2020) examining the intensive and problem-
atic social media use and well-being of adolescents in twenty-nine countries, the result of
this study is consistent with the result that problematic social media users are at a lower
welfare level in all areas compared to non-problematic users in terms of all countries.
In addition to these studies, in another study conducted by Marttila etal. (2021), it was
determined that social media addiction is negatively related to life satisfaction in the first
place. In some studies, they found a negative significant relationship between internet
addiction and life satisfaction (Batıgün & Kılıç, 2011; Caplan, 2005; Dilsiz & Kandemir,
2020; Esen & Siyez, 2011; Hinsch & Sheldon, 2013; Morsünbül, 2014).
Fig. 5 The metric values of the methods according to the estimation of the test data
Fig. 6 Happiness prediction with REG method

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The increasing use of social media in recent years may cause psychological, physical,
and social media addiction problems in people. It has been revealed that social networks,
which significantly affect the lives of individuals, can also affect the life satisfaction of
social individuals. Life satisfaction can be said to be the positive values and thoughts
that people give to their own lives in general. Since social media addiction has a negative
meaning and life satisfaction has a positive meaning, it has been observed that social media
addiction, which is negative, has effects on life satisfaction. According to Kara (2017), in
recent years, the fact that people want to use social networks to spend a fun time, in gen-
eral, brings along some negative situations such as envy, individualization, and hopeless-
ness. This situation can also affect individuals’ life satisfaction depending on social media.
A significant relationship between social media addiction and happiness is present in
our study. As social media addiction increases, the level of happiness decreases. Studies
report that the concept of happiness is subjective and that social media addiction increases
relative happiness and well-being, but pushes young people away from social life and iso-
lates them, therefore creating unhappiness in the long term (Shek & Yu, 2016). Similar
results were found in the literature (Balcı & Kocak, 2017; Eroğlu & Bayraktar, 2017; Ford
& Mauss, 2014; Süler, 2016).
In our study, the hypothesis that life satisfaction has a mediating role in the effect of
social media addiction on happiness was confirmed. Ford and Mauss (2014) mentioned
three main features between being happy and social media. The first is that the feeling of
unhappiness is felt more when the need to be happy cannot be met, the second is that the
path to happiness is not known, and the third is the negative consequences of constantly
focusing on happiness and striving for happiness (Ford & Mauss, 2014). The feeling of
life satisfaction and happiness is confused with the behaviors of freedom and acceptance
in social media. Freedom and constantly waiting for the admiration of others and wanting
the continuity of this are completely contrary to the concept of “happiness.” It is seen that
individuals who achieve happiness on social media cannot be happy in real life (Frison &
Fig. 7 Determining the contributions of variables to the model for happiness estimation with Shapley val-
ues (SHAP plot according to the most accurate parameter values estimated in the xgboost method (lambda
= 0.5, alpha = 3, eta = 1, nrounds = 1000))

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Eggermont, 2017). When the research conducted by Balcı and Koçak considering the life
satisfaction of individuals is evaluated, it reveals a negative relationship between happiness
levels and frequency of social media use and indirectly active internet use (Balcı & Kocak,
2017). As the happiness levels of internet users increase based on their life satisfaction
levels, a decrease in the duration of their social media use manifests itself. In light of the
findings, individuals with low levels of happiness, depending on their level of life satisfac-
tion, use social media more frequently daily because it is a habit. In addition, in their study,
while their happiness levels increase linearly as their life satisfaction levels increase, there
is a regression and decrease in the frequency of social media use to evaluate leisure time
(Balcı & Kocak, 2017). Considering our study findings and the studies previously con-
ducted, it shows that social media addiction negatively affects the happiness level and life
satisfaction of individuals.
Happiness levels of individuals can be affected by many factors. In this study, the effect
of social media addiction, which is an important problem of today, was examined and the
effect of life satisfaction on happiness was revealed. It has been revealed that individuals
should pay attention to the variables that affect the level of happiness while paying atten-
tion to these two variables. In addition, it has been determined that they should give impor-
tance to social media addiction and happiness in the category of variables that can affect
happiness in studies on happiness.
Conclusion
As the level of social media addiction increases, life satisfaction and happiness levels
decrease. Longitudinal studies on social media addiction are recommended. Longitudinal
studies on social media addiction are recommended. In this study, happiness has been esti-
mated by using variables with machine learning approach. The best estimation was made
with elastic net regression model from machine learning approaches. It is also recom-
mended to make predictions with new machine learning approach models to be added to
the literature.
Acknowledgements We thank all adult participants who participated in the study.
Author Contribution Study design: Çiftci Necmettin, Yıldız Metin. Data collection: Çiftci Necmettin,
Yıldız Metin. Data analysis: Çiftci Necmettin, Yıldız Metin. Manuscript writing: Çiftci Necmettin, Yıldız
Metin
Declarations
Ethics Approval All procedures followed were in accordance with the ethical standards of the responsible
committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975,
as revised in 2000. Approval was obtained from Muş Alparslan University Scientific Research and Publica-
tion Ethics Committee for the research (Date and Number: 01.03.2022-42263) was performed. Verbal con-
sent to participate in the research was obtained from the individuals by giving information about the purpose
of the research, the method, the time they would spare for the research, and by declaring that participating in
the research would not do any harm and that the participation was completely voluntary.
Consent to Participate Voluntary consent was obtained from the participants participating in the study.
Consent for Publication Publication permission was obtained from the participants.
Conflict of Interest The authors declare no competing interests.

International Journal of Mental Health and Addiction
1 3
References
Aluş, Y., & Selçukkaya, S. (2015). Türk ailesinde mutluluk algısı ve değerleri. Sosyal ve Kültürel
Araştırmalar Dergisi, 1(2), 151–175.
Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen,
S. (2016). The relationship between addictive use of social media and video games and symptoms
of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors,
30(2), 252–262.
Ansari, H., Ansari-Moghaddam, A., Mohammadi, M., Peyvand, M., Amani, Z., & Arbabisarjou, A.
(2016). Internet addiction and happiness among medical sciences students in southeastern Iran.
Health Scope, 5(2), e33600. https:// doi. org/ 10. 5812/ jheal thsco pe. 20166
Ayyıldız, F., & Şahin, G. (2022). Effect of social media addiction on eating behavior, body weight and
life satisfaction during pandemic period. British Food Journal, 124(9), 2980–2992. https:// doi. org/
10. 1108/ BFJ- 01- 2022- 0018
Balcı, Ş., & Kocak, M. C. (2017). Sosyal medya kullanımı ile yaşam doyumu arasındaki ilişki: üni-
versite öğrencileri üzerinde bir araştırma. 1 (pp. 34–45). Uluslararası İletişimde Yeni Yönelimler
Konferansı.
Batıgün, A. D., & Kılıç, N. (2011). İnternet bağımlılığı ile kişilik özellikleri, sosyal destek, psikolojik
belirtiler ve bazı sosyo-demografik değişkenler arasındaki ilişkiler. Turk Psikoloji Dergisi, 26(67),
1–10.
Boer, M., Van Den Eijnden, R. J., Boniel-Nissim, M., Wong, S.-L., Inchley, J. C., Badura, P., Craig, W. M.,
Gobina, I., Kleszczewska, D., & Klanšček, H. J. (2020). Adolescents’ intense and problematic social
media use and their well-being in 29 countries. Journal of adolescent health, 66(6), S89–S99. https://
doi. org/ 10. 1016/j. jadoh ealth. 2020. 02. 014
Brailovskaia, J., Truskauskaite-Kuneviciene, I., Kazlauskas, E., & Margraf, J. (2021). The patterns of
problematic social media use (SMU) and their relationship with online flow, life satisfaction, depres-
sion, anxiety and stress symptoms in Lithuania and in Germany. Current Psychology, 42, 3713–3724.
https:// doi. org/ 10. 1007/ s12144- 021- 01711-w
Caplan, S. E. (2005). A social skill account of problematic Internet use. Journal of communication, 55(4),
721–736. https:// doi. org/ 10. 1111/j. 1460- 2466. 2005. tb030 19.x
Cohen, J. (1988). Statistical power analysis for the Behavioral Sciences (3rd ed.). Lawrence Erlbaum
Associates.
Collier, J. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques (1st
ed.). https:// doi. org/ 10. 4324/ 97810 03018 414
Çevik, N. K., & Korkmaz, O. (2014). Türkiye’de yaşam doyumu ve iş doyumu arasindaki ilişkinin iki
değişkenli sirali probit model analizi. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi,
7(1), 126–145.
Çiftçi, H. (2018). Üniversite öğrencilerinde sosyal medya bağımlılığı. MANAS Sosyal Araştırmalar Dergisi,
7(4), 417–434.
Dağlı, A., & Baysal, N. (2016). Yaşam doyumu ölçeğinin türkçe’ye uyarlanmasi: geçerlik ve güvenirlik
çalişmasi. Elektronik Sosyal Bilimler Dergisi, 15(59), 1250–1263. https:// doi. org/ 10. 17755/ esosd er.
263229
Demirci, İ. (2019). Bergen Sosyal Medya Bağımlılığı Ölçeğinin Türkçeye uyarlanması, depresyon ve anksi-
yete belirtileriyle ilişkisinin değerlendirilmesi. Anadolu Psikiyatri Dergisi, 20, 15–22. https:// doi. org/
10. 5455/ apd. 41585
Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of
personality assessment, 49(1), 71–75. https:// doi. org/ 10. 1207/ s1532 7752j pa4901_ 13
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of pro-
gress. Psychological Bulletin, 125(2), 276–303.
Dilsiz, N. B., & Kandemir, M. (2020). Lise öğrencilerinde sosyal medya bağımlılığının bağlanma stilleri ve
yaşam doyumu ile ilişkisinin incelenmesi. Online Journal of Technology Addiction and Cyberbullying,
7(2), 32–51.
Doğan, T. (2013). Beş faktör kişilik özellikleri ve öznel iyi oluş. Doğuş Üniversitesi Dergisi, 14(1), 56–64.
Doğan, T., & Akıncı Çötok, N. (2011). Oxford mutluluk ölçeği kısa formunun Türkçe uyarlaması: Geçerlik
ve güvenirlik çalışması. Türk psikolojik danışma ve rehberlik dergisi, 4(36), 165–172.
Eroğlu, A., & Bayraktar, S. (2017). İnternet bağımlılığı ile ilişkili değişkenlerin incelenmesi. International
Journal of Social Sciences and Education Research, 3(1), 184–199.
Eryılmaz, A. (2016). Mutluluğun Başucu Kitabı, 2. Pegem Akademi Yayıncılık.
Esen, E., & Siyez, D. M. (2011). Ergenlerde internet bağımlılığını yordayan psiko-sosyal değişkenlerin
incelenmesi. Türk psikolojik danışma ve rehberlik dergisi, 4(36), 127–138.

International Journal of Mental Health and Addiction
1 3
Ford, B., & Mauss, I. (2014). The paradoxical effects of pursuing positive emotion. J. Gruber, J. T.
Moskowitz, Positive emotion: Integrating the light sides and dark sides (363-382).
Frison, E., & Eggermont, S. (2017). Browsing, posting, and liking on Instagram: The reciprocal relation-
ships between different types of Instagram use and adolescents’ depressed mood. Cyberpsychology,
Behavior, and Social Networking, 20(10), 603–609. https:// doi. org/ 10. 1089/ cyber. 2017. 0156
Gürbüz, S. (2019). AMOS ile Yapısal Eşitlik Modellemesi, 1 (pp. 33–44). Baskı.
Halverson, R., Kallio, J., Hackett, S., & Halverson, E. (2016). Participatory culture as a model for how
new media technologies can change public schools. In WCER Working Paper No. 2016-7. Wiscon-
sin Center for Education Research.
Hills, P., & Argyle, M. (2002). The Oxford Happiness Questionnaire: A compact scale for the meas-
urement of psychological well-being. Personality and Individual Differences, 33(7), 1073–1082.
https:// doi. org/ 10. 1016/ S0191- 8869(01) 00213-6
Hinsch, C., & Sheldon, K. M. (2013). The impact of frequent social Internet consumption: Increased
procrastination and lower life satisfaction. Journal of Consumer Behaviour, 12(6), 496–505. https://
doi. org/ 10. 1002/ cb. 1453
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Con-
ventional criteria versus new alternatives. Structural Equation Modeling, A Multidisciplinary Jour-
nal, 6(1), 1–55. https:// doi. org/ 10. 1080/ 10705 51990 95401 18
Kara, F. (2017). Yaşam doyumunda sosyal medyanın rolü: Konya örneği (Publication Number 471197).
[Yüksek Lisans Tezi, Selçuk Üniversitesi]. Yök Tez Merkezi https:// tez. yok. gov. tr/ Ulusa lTezM
erkezi/ tezSo rguSo nucYe ni. jsp
Karagöz, Y. (2019). SPSS-AMOS-META uygulamalı istatistiksel analizler. Nobel Yayıncılık.
Kim, H. H. S. (2017). The impact of online social networking on adolescent psychological well-being
(WB): A population-level analysis of Korean school-aged children. International Journal of Ado-
lescence and Youth, 22(3), 364–376. https:// doi. org/ 10. 1080/ 02673 843. 2016. 11971 35
Kozma, A., & Stones, M. (1980). The measurement of happiness: Development of the Memorial Uni-
versity of Newfoundland Scale of Happiness (MUNSH). Journal of Gerontology, 35(6), 906–912.
https:// doi. org/ 10. 1093/ geronj/ 35.6. 906
Kumpasoğlu, G. B., Eltan, S., Merdan-Yıldız, E. D., & Batıgün, A. D. (2021). Mediating role of life
satisfaction and death anxiety in the relationship between dark triad and social media addiction.
Personality and Individual Differences, 172, 110606. https:// doi. org/ 10. 1016/j. paid. 2020. 110606
Kutlu, M., Savcı, M., Demir, Y., & Aysan, F. (2016). Young İnternet Bağımlılığı Testi Kısa Formunun
Türkçe uyarlaması: Üniversite öğrencileri ve ergenlerde geçerlilik ve güvenilirlik çalışması. Anad-
olu Psikiyatri Dergisi, 17(1), 69–76. https:// doi. org/ 10. 5455/ apd. 190501
Lee, J. Y., & Lee, M. H. (2022). Structural model of retention intention of nurses in small- and medium-
sized hospitals: Based on Herzberg&rsquo;s Motivation-Hygiene Theory. Healthcare, 10(3), 502.
https:// doi. org/ 10. 3390/ healt hcare 10030 502
Li, W., O’Brien, J., Snyder, S., & Howard, M. (2015). Characteristics of internet addiction/pathologi-
cal internet use in U.S. university students: A qualitative-method investigation. PloS one, 10(2),
e0117372. https:// doi. org/ 10. 1371/ journ al. pone. 01173 72
Longstreet, P., & Brooks, S. (2017). Life satisfaction: A key to managing internet & social media addic-
tion. Technology in Society, 50, 73–77. https:// doi. org/ 10. 1016/j. techs oc. 2017. 05. 003
Marttila, E., Koivula, A., & Räsänen, P. (2021). Does excessive social media use decrease subjective
well-being? A longitudinal analysis of the relationship between problematic use, loneliness and life
satisfaction. Telematics and Informatics, 59, 1–11. https:// doi. org/ 10. 1016/j. tele. 2020. 101556
Morsünbül, Ü. (2014). İnternet bağımlılığının bağlanma stilleri, kişilik özellikleri, yalnızlık ve yaşam
doyumu ile ilişkisi. International Journal of Human Sciences, 11(1), 357–372. https:// doi. org/ 10.
14687/ ijhs. v11i1. 2727
Mottaghi, S., Poursheikhali, H., & Shameli, L. (2019). Empathy, compassion fatigue, guilt and second-
ary traumatic stress in nurses. Nursing Ethics, 27(2), 494–504. https:// doi. org/ 10. 1177/ 09697 33019
851548
Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009). College students’ social networking experi-
ences on Facebook. Journal of Applied Developmental Psychology, 30(3), 227–238. https:// doi. org/
10. 1016/j. appdev. 2008. 12. 010
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. https:// doi. org/
10. 1016/j. chb. 2016. 03. 084
Shek, D. T., & Yu, L. (2016). Adolescent internet addiction in Hong Kong: Prevalence, change, and cor-
relates. Journal of Pediatric and Adolescent Gynecology, 29(1), S22–S30. https:// doi. org/ 10. 1016/j.
jpag. 2015. 10. 005

International Journal of Mental Health and Addiction
1 3
Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit: A uses and gratifica-
tion approach to unbundling feature use. Computers in Human Behavior, 27(6), 2322–2329. https://
doi. org/ 10. 1016/j. chb. 2011. 07. 011
Sun, Y., & Zhang, Y. (2021). A review of theories and models applied in studies of social media addiction
and implications for future research. Addictive Behaviors, 114, 106699. https:// doi. org/ 10. 1016/j. add-
beh. 2020. 106699
Süler, M. (2016). Akıllı telefon bağımlılığının öznel mutluluk düzeyine etkisinin çeşitli değişkenler açısından
incelenmesi (Publication Number 431349). [Yüksek Lisans Tezi, Sakarya Üniversitesi]. Yök Tez
Merkezi https:// tez. yok. gov. tr/ Ulusa lTezM erkezi/ tezSo rguSo nucYe ni. jsp
Tutgun-Ünal, A., & Deniz, L. (2016). Üniversite öğrencilerinin sosyal medya bağımlılığının incelenmesi.
Route Educational and Social Science Journal, 3(2), 155–181. https:// doi. org/ 10. 17121/ ressj ournal.
488
Vandenbrouckel, J. P., von Elm, E., Altman, D. G., Gotzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C.,
Schlesselman, J. J., & Egger, M. (2007). Strengthening the Reporting of Observational Studies in Epi-
demiology (STROBE): Explanation and elaboration. PLoS Medicine, 4(10), 1628–1655.
Yoon, S., Kim, H. Y., & Kim, S. R. (2021). A prediction model of health-related quality of life in young
adult patients with stroke. Journal of clinical nursing, 30(13-14), 2023–2035. https:// doi. org/ 10. 1111/
jocn. 15755
Zivnuska, S., Carlson, J. R., Carlson, D. S., Harris, R. B., & Harris, K. J. (2019). Social media addiction
and social media reactions: The implications for job performance. The Journal of Social Psychology,
159(6), 746–760. https:// doi. org/ 10. 1080/ 00224 545. 2019. 15787 25
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