Association rules - social media’s impact on mental health
Joanna Jędrzejewska
Introduction
Mental health issues have become increasingly prevalent in
contemporary society, with various factors contributing to their rise.
In recent years, the relationship between mental health and social media
usage has garnered attention due to the pervasive nature of online
platforms.The effects of bigger interest in internet websites and
applications have been linked to poor sleep patterns, depression, and
anxiety (Meier, Reinecke, 2021).
This study aims to explore the
intricate connection between social media usage patterns and different
mental health indicators among individuals. Understanding this
relationship is crucial for devising informed interventions and
strategies to promote mental well-being.
Relevance
The economic implications of mental health issues are significant, affecting both individuals and societies at large. Mental health challenges can lead to decreased productivity, due to occurrence of absenteeism or presenteeism (Bloom, et al., 2011), as well as unsatisfactory standard of living. Understanding its impact on mental health is vital for economists seeking to assess the broader economic consequences, and to other experts and members of society. Analyzing the association between social media habits and mental health indicators contributes to a comprehensive understanding of the societal and economic ramifications.
Data Description
The dataset “Social Media and Mental Health” encompasses a diverse set of variables capturing various aspects of individuals’ lives. These include demographic information such as age, gender, relationship status, occupation status and organizations affiliated with responders. Additionally, the dataset delves into the specifics of social media usage, including the types of platforms visited by the individuals, hours spent, and the usage without any specific purpose. Mental health is assessed through indicators like distraction, restlessness, concentration difficulty, daily interests fluctuations and sleeping problems, providing a holistic view of participants’ well-being. The survey also refers to the topic of self-image, by questions related to comparing to other people and feelings related, being worried, and seeking validation. The dataset can be found under the link: https://www.kaggle.com/datasets/souvikahmed071/social-media-and-mental-health.
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## abbreviate, write## Warning: pakiet 'arulesViz' został zbudowany w wersji R 4.3.2head(mental_health)## Timestamp X1..What.is.your.age. X2..Gender X3..Relationship.Status
## 1 4/18/2022 19:18:47 21 Male In a relationship
## 2 4/18/2022 19:19:28 21 Female Single
## 3 4/18/2022 19:25:59 21 Female Single
## 4 4/18/2022 19:29:43 21 Female Single
## 5 4/18/2022 19:33:31 21 Female Single
## 6 4/18/2022 19:33:48 22 Female Single
## X4..Occupation.Status X5..What.type.of.organizations.are.you.affiliated.with.
## 1 University Student University
## 2 University Student University
## 3 University Student University
## 4 University Student University
## 5 University Student University
## 6 University Student University
## X6..Do.you.use.social.media.
## 1 Yes
## 2 Yes
## 3 Yes
## 4 Yes
## 5 Yes
## 6 Yes
## X7..What.social.media.platforms.do.you.commonly.use.
## 1 Facebook, Twitter, Instagram, YouTube, Discord, Reddit
## 2 Facebook, Twitter, Instagram, YouTube, Discord, Reddit
## 3 Facebook, Instagram, YouTube, Pinterest
## 4 Facebook, Instagram
## 5 Facebook, Instagram, YouTube
## 6 Facebook, Twitter, Instagram, YouTube, Discord, Pinterest, TikTok
## X8..What.is.the.average.time.you.spend.on.social.media.every.day.
## 1 Between 2 and 3 hours
## 2 More than 5 hours
## 3 Between 3 and 4 hours
## 4 More than 5 hours
## 5 Between 2 and 3 hours
## 6 Between 2 and 3 hours
## X9..How.often.do.you.find.yourself.using.Social.media.without.a.specific.purpose.
## 1 5
## 2 4
## 3 3
## 4 4
## 5 3
## 6 4
## X10..How.often.do.you.get.distracted.by.Social.media.when.you.are.busy.doing.something.
## 1 3
## 2 3
## 3 2
## 4 2
## 5 5
## 6 4
## X11..Do.you.feel.restless.if.you.haven.t.used.Social.media.in.a.while.
## 1 2
## 2 2
## 3 1
## 4 1
## 5 4
## 6 2
## X12..On.a.scale.of.1.to.5..how.easily.distracted.are.you.
## 1 5
## 2 4
## 3 2
## 4 3
## 5 4
## 6 3
## X13..On.a.scale.of.1.to.5..how.much.are.you.bothered.by.worries.
## 1 2
## 2 5
## 3 5
## 4 5
## 5 5
## 6 4
## X14..Do.you.find.it.difficult.to.concentrate.on.things.
## 1 5
## 2 4
## 3 4
## 4 3
## 5 5
## 6 3
## X15..On.a.scale.of.1.5..how.often.do.you.compare.yourself.to.other.successful.people.through.the.use.of.social.media.
## 1 2
## 2 5
## 3 3
## 4 5
## 5 3
## 6 4
## X16..Following.the.previous.question..how.do.you.feel.about.these.comparisons..generally.speaking.
## 1 3
## 2 1
## 3 3
## 4 1
## 5 3
## 6 4
## X17..How.often.do.you.look.to.seek.validation.from.features.of.social.media.
## 1 2
## 2 1
## 3 1
## 4 2
## 5 3
## 6 3
## X18..How.often.do.you.feel.depressed.or.down.
## 1 5
## 2 5
## 3 4
## 4 4
## 5 4
## 6 3
## X19..On.a.scale.of.1.to.5..how.frequently.does.your.interest.in.daily.activities.fluctuate.
## 1 4
## 2 4
## 3 2
## 4 3
## 5 4
## 6 2
## X20..On.a.scale.of.1.to.5..how.often.do.you.face.issues.regarding.sleep.
## 1 5
## 2 5
## 3 5
## 4 2
## 5 1
## 6 4The survey offered possible responses from a range prepared by the survey creator. It consists of answers in the form of a text (demographics), or answers on a numerical scale (impact on mental health). The level of impact of social media use was presented on a scale of 1 to 5, where 1 marks the lowest impact, 3 the average impact, while 5 indicates the highest possible impact on the factor indicated in the question.
Transforming the dataset
For a clearer insight into the data, the names of the labels were changed. The variables were changed into factors in further step.
mental_health<-mental_health %>%
rename(
age=2,
gender=3,
relationship_status=4,
occupation_status=5,
organisation=6,
social_media_use=7,
social_media_types=8,
social_media_hours=9,
social_media_reasonless=10,
distraction_freq=11,
restlessness=12,
distraction_level=13,
worried_level=14,
concentration_difficulty=15,
comparing_self=16,
feeling_comparing=17,
validation_seek=18,
sad_freq=19,
daily_interest=20,
issues_sleep=21
)
head(mental_health)## Timestamp age gender relationship_status occupation_status
## 1 4/18/2022 19:18:47 21 Male In a relationship University Student
## 2 4/18/2022 19:19:28 21 Female Single University Student
## 3 4/18/2022 19:25:59 21 Female Single University Student
## 4 4/18/2022 19:29:43 21 Female Single University Student
## 5 4/18/2022 19:33:31 21 Female Single University Student
## 6 4/18/2022 19:33:48 22 Female Single University Student
## organisation social_media_use
## 1 University Yes
## 2 University Yes
## 3 University Yes
## 4 University Yes
## 5 University Yes
## 6 University Yes
## social_media_types
## 1 Facebook, Twitter, Instagram, YouTube, Discord, Reddit
## 2 Facebook, Twitter, Instagram, YouTube, Discord, Reddit
## 3 Facebook, Instagram, YouTube, Pinterest
## 4 Facebook, Instagram
## 5 Facebook, Instagram, YouTube
## 6 Facebook, Twitter, Instagram, YouTube, Discord, Pinterest, TikTok
## social_media_hours social_media_reasonless distraction_freq restlessness
## 1 Between 2 and 3 hours 5 3 2
## 2 More than 5 hours 4 3 2
## 3 Between 3 and 4 hours 3 2 1
## 4 More than 5 hours 4 2 1
## 5 Between 2 and 3 hours 3 5 4
## 6 Between 2 and 3 hours 4 4 2
## distraction_level worried_level concentration_difficulty comparing_self
## 1 5 2 5 2
## 2 4 5 4 5
## 3 2 5 4 3
## 4 3 5 3 5
## 5 4 5 5 3
## 6 3 4 3 4
## feeling_comparing validation_seek sad_freq daily_interest issues_sleep
## 1 3 2 5 4 5
## 2 1 1 5 4 5
## 3 3 1 4 2 5
## 4 1 2 4 3 2
## 5 3 3 4 4 1
## 6 4 3 3 2 4#Variables as factor
mental_health[] <- lapply(mental_health, factor)A given operation gave the opportunity to handle the data more efficiently.
Methods
Association rule
Association rule is employed to uncover hidden patterns and relationships within the dataset. It is a powerful technique that enables the identification of relationships among multiple variables simultaneously (Xiao Hu, Weng-Lam Cheong, Kai-Wah Chu, 2018). This method is particularly suitable for revealing associations between categorical variables, making it ideal for analyzing social media habits and mental health indicators. The Apriori algorithm (Agrawal, Srikant, 1994) is utilized for rule mining in large transaction databases to identify interesting relationships between different attributes, offering valuable insights into the interconnectedness of variables.
Sections
For purpose of the research, the data was separated into two
sections:
1.Deconcentration
2.Self-image
Both of the sets consisted of demographic information, with variables chosen by the author of the study.
Deconcentration
The fist set df1_df contained information about using social media, types of platforms visited by the individuals, hours spent on social media, usage without specific purpose, frequency of distraction, level of getting distracted easily, the feeling of restlessness without social media, concentration difficulty, feeling bothered by worries, fluctuation of interest in daily activities and frequency of sleeping problems.
df1_df<- mental_health[,c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,20,21)]
matrix1 <- df1_df## Number of rows: 481## Number of columns: 16As there is 481 rows and 16 columns, the support and confidence values were set to 75% for support and 0.01 for confidence.
ruleParameters1 <- list(supp = 0.01, conf = 0.75, maxlen = 16)
associationRules1 <- apriori(matrix1, parameter = ruleParameters1)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.75 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 16 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 4
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[255 item(s), 481 transaction(s)] done [0.00s].
## sorting and recoding items ... [116 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10 11 done [0.06s].
## writing ... [203343 rule(s)] done [0.05s].
## creating S4 object ... done [0.11s].plot(associationRules1, method = "graph", measure = "support", shading = "lift", main = "Association Rules Graph")## Warning: Unknown control parameters: main## Available control parameters (with default values):
## layout = stress
## circular = FALSE
## ggraphdots = NULL
## edges = <environment>
## nodes = <environment>
## nodetext = <environment>
## colors = c("#EE0000FF", "#EEEEEEFF")
## engine = ggplot2
## max = 100
## verbose = FALSE## Warning: Too many rules supplied. Only plotting the best 100 using 'lift'
## (change control parameter max if needed).
summary(associationRules1)## set of 203343 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5 6 7 8 9 10 11
## 1 197 4176 26958 58382 59284 35067 14180 4227 807 64
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 6.000 5.774 7.000 11.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01039 Min. :0.7500 Min. :0.01039 Min. : 0.7547
## 1st Qu.:0.01039 1st Qu.:0.8333 1st Qu.:0.01247 1st Qu.: 1.0063
## Median :0.01247 Median :1.0000 Median :0.01455 Median : 1.6473
## Mean :0.01568 Mean :0.9268 Mean :0.01712 Mean : 2.3255
## 3rd Qu.:0.01663 3rd Qu.:1.0000 3rd Qu.:0.01871 3rd Qu.: 3.0833
## Max. :0.99376 Max. :1.0000 Max. :1.00000 Max. :30.0625
## count
## Min. : 5.000
## 1st Qu.: 5.000
## Median : 6.000
## Mean : 7.541
## 3rd Qu.: 8.000
## Max. :478.000
##
## mining info:
## data ntransactions support confidence
## matrix1 481 0.01 0.75
## call
## apriori(data = matrix1, parameter = ruleParameters1)Due to such a big number of rules, those that are above the 3rd quartile were filtered.
associationRules1<- subset(associationRules1, lift >= 3.0833)
rules1_conf<-sort(associationRules1, by="confidence", decreasing=TRUE)
inspect(head(rules1_conf))## lhs rhs support confidence coverage lift count
## [1] {social_media_types=Facebook, Twitter, Instagram, YouTube, Snapchat, Discord, TikTok} => {worried_level=5} 0.01039501 1 0.01039501 3.317241 5
## [2] {social_media_types=Facebook, Instagram, YouTube, Snapchat, Discord, Reddit, Pinterest} => {worried_level=4} 0.01039501 1 0.01039501 3.700000 5
## [3] {age=34} => {occupation_status=Salaried Worker} 0.01247401 1 0.01247401 3.643939 6
## [4] {age=35} => {occupation_status=Salaried Worker} 0.01663202 1 0.01663202 3.643939 8
## [5] {age=17} => {occupation_status=School Student} 0.01871102 1 0.01871102 9.816327 9
## [6] {age=47} => {occupation_status=Salaried Worker} 0.03326403 1 0.03326403 3.643939 16Rules 1 and 2 indicate that individuals who use a combination of Facebook, Twitter, Instagram, YouTube, Snapchat, Discord, and TikTok (Rule 1), or Facebook, Instagram, YouTube, Snapchat, Discord, Reddit, and Pinterest (Rule 2) exhibit a worried level of 5. This suggests a strong association between specific social media usage patterns and a feeling worried. Rules 3 to 5 reveal that individuals of specific ages (34, 35, and 17) have a chance of having certain occupation statuses (salaried worker for ages 34 and 35, and school student for age 17). This indicates a strong relationship between age and occupation status. Rule 5 specifically highlights that individuals aged 17 are strongly associated with the occupation status of being a school student. Rule 6 suggests a strong association between individuals aged 47 and the occupation status of being a salaried worker.
rules1_lift<-sort(associationRules1, by="lift", decreasing=TRUE)
inspect(head(rules1_lift))## lhs rhs support confidence coverage lift count
## [1] {gender=Female,
## occupation_status=Salaried Worker,
## worried_level=2,
## concentration_difficulty=2} => {age=47} 0.01039501 1.0000000 0.01039501 30.06250 5
## [2] {gender=Female,
## relationship_status=Married,
## occupation_status=Salaried Worker,
## worried_level=2,
## concentration_difficulty=2} => {age=47} 0.01039501 1.0000000 0.01039501 30.06250 5
## [3] {gender=Female,
## occupation_status=Salaried Worker,
## social_media_use=Yes,
## worried_level=2,
## concentration_difficulty=2} => {age=47} 0.01039501 1.0000000 0.01039501 30.06250 5
## [4] {gender=Female,
## relationship_status=Married,
## occupation_status=Salaried Worker,
## social_media_use=Yes,
## worried_level=2,
## concentration_difficulty=2} => {age=47} 0.01039501 1.0000000 0.01039501 30.06250 5
## [5] {gender=Female,
## relationship_status=Married,
## worried_level=2,
## concentration_difficulty=2} => {age=47} 0.01039501 0.8333333 0.01247401 25.05208 5
## [6] {gender=Female,
## relationship_status=Married,
## social_media_use=Yes,
## worried_level=2,
## concentration_difficulty=2} => {age=47} 0.01039501 0.8333333 0.01247401 25.05208 5Rules indicate a strong association between specific combinations of gender (Female), occupation status (Salaried Worker), worried level (2), and concentration difficulty (2) with the prediction of age 47.
rules1_support<-sort(associationRules1, by="support", decreasing=TRUE)
inspect(head(rules1_support))## lhs rhs support confidence coverage lift count
## [1] {organisation=Private} => {occupation_status=Salaried Worker} 0.10602911 0.8500000 0.1247401 3.097348 51
## [2] {organisation=Private,
## social_media_use=Yes} => {occupation_status=Salaried Worker} 0.10395010 0.8474576 0.1226611 3.088084 50
## [3] {distraction_freq=5,
## distraction_level=5} => {concentration_difficulty=5} 0.09771310 0.8103448 0.1205821 3.575925 47
## [4] {social_media_use=Yes,
## distraction_freq=5,
## distraction_level=5} => {concentration_difficulty=5} 0.09771310 0.8103448 0.1205821 3.575925 47
## [5] {gender=Male,
## relationship_status=Married} => {occupation_status=Salaried Worker} 0.08939709 0.8958333 0.0997921 3.264362 43
## [6] {gender=Male,
## relationship_status=Married,
## social_media_use=Yes} => {occupation_status=Salaried Worker} 0.08939709 0.8958333 0.0997921 3.264362 43Rules 1 and 2 highlight a strong association between individuals working in private organizations and the occupation status being a salaried worker. Rules 3 and 4 indicate a notable association between high distraction frequency (5) and high distraction level (5) with the concentration difficulty being 5. This suggests that individuals who experience frequent and intense distractions are more likely to face concentration difficulties. Rules 5 and 6 emphasize a strong association between males who are married, especially those who use social media, and the occupation status being a salaried worker. This points to a correlation between gender, marital status, social media usage, and the likelihood of having a salaried occupation.
Self-image
The second set df2_df contained information about using social media, types of platforms visited by the individuals, hours spent on social media, comparing themselves to other people, feelings related to comparing themselves, seeking for validation, frequency of feeling depressed or down, the fluctuation of interest in daily activities and frequency of sleeping problems.
df2_df<- mental_health[,c(2,3,4,5,6,7,8,9,16,17,18,19,20,21)]
matrix2 <- df2_df## Number of rows: 481## Number of columns: 14The support and confidence values were set to 75% for support and 0.01 for confidence.
ruleParameters2 <- list(supp = 0.01, conf = 0.75, maxlen = 14)
associationRules2 <- apriori(matrix2, parameter = ruleParameters2)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.75 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 14 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 4
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[245 item(s), 481 transaction(s)] done [0.00s].
## sorting and recoding items ... [106 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10 done [0.03s].
## writing ... [76200 rule(s)] done [0.02s].
## creating S4 object ... done [0.04s].plot(associationRules2, method = "graph", measure = "support", shading = "lift", main = "Association Rules Graph")## Warning: Unknown control parameters: main## Available control parameters (with default values):
## layout = stress
## circular = FALSE
## ggraphdots = NULL
## edges = <environment>
## nodes = <environment>
## nodetext = <environment>
## colors = c("#EE0000FF", "#EEEEEEFF")
## engine = ggplot2
## max = 100
## verbose = FALSE## Warning: Too many rules supplied. Only plotting the best 100 using 'lift'
## (change control parameter max if needed).
summary(associationRules2)## set of 76200 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5 6 7 8 9 10
## 1 178 3051 15708 27264 20972 7635 1274 107 10
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 5.000 5.239 6.000 10.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01039 Min. :0.7500 Min. :0.01039 Min. : 0.7827
## 1st Qu.:0.01039 1st Qu.:0.8333 1st Qu.:0.01247 1st Qu.: 1.0063
## Median :0.01247 Median :1.0000 Median :0.01455 Median : 1.5094
## Mean :0.01735 Mean :0.9303 Mean :0.01887 Mean : 1.8214
## 3rd Qu.:0.01871 3rd Qu.:1.0000 3rd Qu.:0.01871 3rd Qu.: 1.7250
## Max. :0.99376 Max. :1.0000 Max. :1.00000 Max. :17.8148
## count
## Min. : 5.000
## 1st Qu.: 5.000
## Median : 6.000
## Mean : 8.345
## 3rd Qu.: 9.000
## Max. :478.000
##
## mining info:
## data ntransactions support confidence
## matrix2 481 0.01 0.75
## call
## apriori(data = matrix2, parameter = ruleParameters2)Due to such a big number of rules, those that are above the 3rd quartile were filtered.
associationRules2 <- subset(associationRules2, lift >= 1.7250)
rules2_conf<-sort(associationRules2, by="confidence", decreasing=TRUE)
inspect(head(rules2_conf))## lhs rhs support confidence coverage lift count
## [1] {social_media_types=Facebook, Instagram, YouTube, Snapchat, Discord, Reddit, Pinterest, TikTok} => {gender=Female} 0.01039501 1 0.01039501 1.828897 5
## [2] {social_media_types=Facebook, Instagram, YouTube, Snapchat, Discord, Reddit, Pinterest} => {gender=Male} 0.01039501 1 0.01039501 2.279621 5
## [3] {age=29} => {gender=Female} 0.01247401 1 0.01247401 1.828897 6
## [4] {age=34} => {occupation_status=Salaried Worker} 0.01247401 1 0.01247401 3.643939 6
## [5] {social_media_types=Facebook, Instagram, YouTube, Snapchat, Pinterest, TikTok} => {gender=Female} 0.01455301 1 0.01455301 1.828897 7
## [6] {social_media_types=Facebook, Twitter, Instagram, YouTube, Snapchat, Discord, Reddit} => {gender=Male} 0.01663202 1 0.01663202 2.279621 8Rules 1, 2, 5, and 6 suggest a strong association between specific combinations of social media types and the prediction of gender. For instance, individuals using a combination of Facebook, Instagram, YouTube, Snapchat, Discord, Reddit, Pinterest, and TikTok are confidently predicted to be female (Rule 1). Similarly, other rules highlight associations between different social media types and the prediction of male or female gender. Rule 3 indicates a strong confidence in predicting female gender for individuals aged 29. This suggests a strong association between age 29 and being female. Rule 4 reveals a strong confidence in predicting a salaried worker occupation status for individuals aged 34.
rules2_lift<-sort(associationRules2, by="lift", decreasing=TRUE)
inspect(head(rules2_lift))## lhs rhs support confidence coverage lift count
## [1] {gender=Male,
## organisation=University,
## comparing_self=5,
## feeling_comparing=5} => {validation_seek=5} 0.01039501 1 0.01039501 17.81481 5
## [2] {gender=Male,
## occupation_status=University Student,
## comparing_self=5,
## feeling_comparing=5} => {validation_seek=5} 0.01247401 1 0.01247401 17.81481 6
## [3] {gender=Male,
## occupation_status=University Student,
## feeling_comparing=5,
## daily_interest=5} => {validation_seek=5} 0.01039501 1 0.01039501 17.81481 5
## [4] {gender=Male,
## occupation_status=University Student,
## comparing_self=5,
## feeling_comparing=5,
## daily_interest=5} => {validation_seek=5} 0.01039501 1 0.01039501 17.81481 5
## [5] {relationship_status=Single,
## occupation_status=University Student,
## comparing_self=5,
## feeling_comparing=5,
## daily_interest=5} => {validation_seek=5} 0.01039501 1 0.01039501 17.81481 5
## [6] {gender=Male,
## organisation=University,
## comparing_self=5,
## feeling_comparing=5,
## sad_freq=5} => {validation_seek=5} 0.01039501 1 0.01039501 17.81481 5Rules 1 and 2 reveal a strong association between male individuals who are university students, exhibit high levels of self-comparison and feeling of comparing, and have a strong tendency to seek validation (Validation Seek=5). Rules 3 and 4 highlight the association between male university students who feel comparing, express fluctuation of daily activities interest, and seek validation. Rule 5 demonstrates a strong association between individuals who are single university students, exhibit high levels of self-comparison, feeling of comparing, express fluctuation of daily activities interest, and seek validation (Validation Seek=5). Rule 6 indicates a strong association between male individuals who are university students, engage in self-comparison, feel comparing, and express sadness, with a strong tendency to seek validation.
rules2_support<-sort(associationRules2, by="support", decreasing=TRUE)
inspect(head(rules2_support))## lhs rhs support confidence coverage lift count
## [1] {relationship_status=Married} => {occupation_status=Salaried Worker} 0.1621622 0.7722772 0.2099792 2.814131 78
## [2] {relationship_status=Married,
## social_media_use=Yes} => {occupation_status=Salaried Worker} 0.1621622 0.7722772 0.2099792 2.814131 78
## [3] {organisation=Private} => {occupation_status=Salaried Worker} 0.1060291 0.8500000 0.1247401 3.097348 51
## [4] {gender=Female,
## occupation_status=University Student,
## daily_interest=4} => {organisation=University} 0.1060291 0.8947368 0.1185031 1.800705 51
## [5] {gender=Female,
## occupation_status=University Student,
## social_media_use=Yes,
## daily_interest=4} => {organisation=University} 0.1060291 0.8947368 0.1185031 1.800705 51
## [6] {organisation=Private,
## social_media_use=Yes} => {occupation_status=Salaried Worker} 0.1039501 0.8474576 0.1226611 3.088084 50Rules 1 and 2 indicate a substantial association between individuals who are married and having a salaried worker occupation. Rule 3 highlights a strong association between individuals working in private organizations and having a salaried worker occupation. Rules 4 and 5 reveal a notable association between female individuals who are university students, express daily interest fluctuation, and are associated with university organizations. Rule 6 emphasizes an association between individuals working in private organizations, using social media, and having a salaried worker occupation.
Conclusions
The research delves into the intricate connection between social media usage patterns and mental health indicators, recognizing the increasing prevalence of mental health issues in society. The study, divided into two sections provides a holistic view of individuals’ well-being through diverse demographic and mental health indicators, as addressing mental health challenges requires a multi-faceted approach that considers individual characteristics, occupational settings, and social interactions. For example, the strong association between male university students engaging in self-comparison, feeling of comparing, and seeking validation underscores the psychological aspects related to self-image and the need for external validation. This information is valuable for designing interventions that address self-esteem and social validation concerns, which can be adapted to the needs and capabilities of different groups of people.
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