Exploring Korean adolescent stress on social media: a semantic network analysis

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Brain, Cognition and Mental Health

Introduction

Desirable mental health is central to adolescent development into healthy adulthood, so multifaceted efforts are urgently needed to properly manage excessive adolescent stress. The 2021 perception rate of stress (38.8%) among Korean adolescents is 32.3% for male students and 45.6% for female students, and it tends to increase as they progress to higher grades (Korea Disease Control and Prevention Agency, 2022). Additionally, 26.8% of Korean adolescents have experienced depression, 12.7% thought of suicide, and 2.2% have attempted suicide (Korea Disease Control and Prevention Agency, 2022). Likewise, approximately 37% of high school students in the United States have experienced periods of persistent feelings of sadness or hopelessness during the past year, and nearly half of all female students in Korea have experienced persistent feelings of sadness or hopelessness in 2019 (CDC, 2021).

Adolescents cannot escape from stress in the fierce competition of modern society. Overstressed adolescents often express their stress through delinquency or violence, causing social problems and, in severe cases, leading to suicidal thoughts (Park, Kang & Lee, 2017). Adolescents who cannot find an appropriate way to cope with stress levels that exceed their personal resources and can threaten their well-being often attempt suicide as a way of escaping reality (Kang & Shin, 2015). Suicidal ideation in adolescents was positively correlated with stress experienced in daily life. Although many previous studies on adolescent stress have been reported, such as factors affecting adolescent stress (Jang, 2022), related problems (Oh & Kweon, 2019), coping methods, and interventions to reduce stress (Lim, 2021), it remains a serious health and social problem.

Korean adolescents spent an average of 285.2 and 397.1 min per day on weekdays and weekends, respectively, on their smartphones (Korea Disease Control and Prevention Agency, 2022). Simultaneously, social media use has markedly increased among adolescents. In the US, the proportion of young people between the ages of 13 and 17 years who have a smartphone has reached 89%, more than doubling over a 6-year period, and 70% of teenagers use social media multiple times in a day, up from one-third of all teenagers in 2012 (Rideout & Robb, 2018). Vernon, Modecki & Barber (2018) analysis of Australian longitudinal data found that 86% of students owned smartphones in Grade 8, increasing to 93% by Grade 11, with increased use of social media communication; additionally, most adolescents rely on smartphones to obtain health information (Chau, Burgermaster & Mamykina, 2018).

Recently, with the rapid spread of smartphones, smart TVs, and mobile Internet and social media, the available data has increased exponentially, leading to an era of big data whereby data are used in various fields, particularly in healthcare (Song, 2013). Thus, researchers collect social media messages to expand their knowledge and analyse the meaning of the data using social media analytics (Song & Ryu, 2015).

Big data in social media is not just a technology to collect, process, and analyse massive amounts of data. The meaning that can be created from such data is more valuable. The core of big data technology is to analyse the pouring information and provide valuable new information and services (Choi, 2015). The information on stress on social media can be extremely useful for adolescents, given the amount of time they spend on social media platforms.

Network analysis is a useful way to derive the characteristics of network types and characterise topics of interest in relation to each other (Kim & Kim, 2016). Semantic networks were used to infer the subjects used in the texts. Semantic Network Analysis (SNA) describes the relationships between related concepts through word co-occurrence analysis. By evaluating the networks that appear, the SNA can highlight the most prominent information in the body of the text (Featherstone et al., 2020). In addition, such analysis and visualisation help to easily understand the knowledge structure and implicit meaning of the phenomenon of interest (Yoon, 2013). Therefore, by analysing and categorising the connectivity of big data-based collection, analysis, and processing, the characteristics and structure of the contents related to stress in adolescents can be identified.

Previous studies

Previous studies that attempted big data-based SNA on adolescents analysed research trends related to childhood and adolescent cancer survivors in South Korea using word co-occurrence network analysis (Kang et al., 2021) and the knowledge structure of students with severe and multiple disabilities (Song, 2018). Another study analysed the perception of sports and physical activity in Korean adolescents through big data analysis over the last 10 years, collating data from Naver, Daum, and Google, which are the most widely used search engines (Park et al., 2020) in Korea, using TEXTOM 4.0. Yet another study collected data from search engines widely used in Korea to identify social media words that express adolescents’ dietary behaviours and identify the associations and types of such words and behaviours. It used text-mining techniques and SNA for related big data collected from the Internet on adolescents’ dietary behaviours (Song et al., 2022). A study on physical activity and exercise in school-age youth was conducted to provide a solution by analysing a large number of scientific articles through text mining (Pans et al., 2021). In the belief that social media plays an important role in adolescents’ life, a study describing the big data approach to social media has also been presented by analysing an ad hoc dataset from the eating disorder forum of a social media website (Moessner et al., 2018).

Considering the long time spent on internet use and the high levels of stress among adolescents, we had difficulty finding a study that investigated adolescent stress using big data-based network analysis of social media in the process of reviewing previous studies. Therefore, this study was designed to provide basic data to establish desirable stress coping strategies for adolescents, based on big data-based network analysis of social media for Korean adolescent stress. This study aimed to (1) identify social media words that express stress in adolescents and (2) investigate the associations between those words and their types.

Materials & Methods

In this study, we used social media big data to analyse adolescents’ awareness of stress. The data collection and analysis processes used in this study are shown in Fig. 1 and this is the same method as described in the previous study (Song et al., 2022). First, we collected data on adolescent stress by crawling online news and blog websites. Then, we extracted keywords using natural language processing (NLP) and performed pre-processing of the extracted keywords. Next, a SNA was performed to understand the relationships among the extracted keywords. For this study, we implemented two Python programs using suitable libraries instead of using a non-free web-based big data analysis solution such as TEXTOM (TheIMC, 2018); one program collects data by web crawling and the other performs the pre-processing of collected data and SNA, except CONCOR analysis. The UCINET package was used for CONCOR analysis and data visualisation.

The study process.

Figure 1: The study process.

Data collection

We collected relevant data from online news and blogs on Naver (2022) and Daum (2022), the largest search engines in Korea. In August 2022, using the search keyword ‘adolescent stress’, we collected 654 news articles from Naver news and 1,654 blog posts from Naver and Daum blogs using a web crawling programme implemented in Python. Although the period for data collection was not specified, more than 90% of the data are from the last 10 years. We overcame the anti-crawling functions of some websites using the Selenium library, which automates web browser interactions in the programme. Duplicate content frequently occurs in online news because several sources provide news articles with the same. Cosine similarity, defined as the cosine of the angle between two word vectors, is widely used for similarity measurement between documents (Rahutomo, Kitasuka & Aritsugi, 2012). We eliminated duplicate content by using cosine similarity.

Pre-processing

We refined the collected data by selecting only nouns, verbs, and adjectives by the unigram method using KoNLPy, an open-source Python library for Korean NLP (Park & Cho, 2014), and excluding stop-words which are commonly used words or unimportant words. Next, we selected the top 30 keywords based on The Term Frequency–Inverse Document Frequency (TF-IDF) values (Boom et al., 2015; Leskovec, Rajaraman & Ullman, 2011), the opinions of a counselling teacher, and a network analysis expert. The TF-IDF is the formal measure of how the occurrences of a given word are concentrated into relatively few documents. TF-IDF is calculated as tf(d,t) idf(d,t), where term frequency tf(d,t) represents the number of appearances of a specific word t in a specific document d. Inverse document frequency idf(d,t) is a factor which diminishes the weight of terms that occur very frequently in the document set and increases the weight of terms that occur rarely; it is represented by log(N/(df(t)+1)), where df(t) is the number of documents in which a specific word t appears. Because there is no quantitative criterion for the number of selected keywords, we have selected top 30 words as in many similar studies (Choi et al., 2022; Jung, 2022; Ye et al., 2022; Park, Lee & Hong, 2023).

For the top 30 keywords, we constructed a frequency table; thereafter, a document term matrix (DTM) (Anandarajan, Hill & Nolan, 2019) was generated to represent the frequency of words per document. DTM can quantify the relationships between words and documents. Subsequently, a co-occurrence matrix (COM) was constructed to represent the relationship between the simultaneous appearances of words in all documents. To simplify the network analysis of the COM, the constructed COM was transformed into a binary matrix using the median of all its elements as a threshold value. If an element was higher than the threshold value, it was changed to 1; otherwise, it was changed to 0.

Semantic network analysis and visualisation

SNA was performed to understand the relationships among the top 30 words related to adolescent stress. Network centralities were calculated to identify important words, and a CONvergence analysis of an iterative CORrelation (CONCOR) analysis was performed to identify groups of words with the same relationship pattern.

We calculated four network centralities of COM: (1) degree centrality—the number of nodes a particular node (Xie, 2005) is connected to; (2) betweenness centrality—a measure of the mediation role of a node in a network; (3) closeness centrality—the inverse of the mean distance to all other nodes, which indicates how close a node is to all other nodes; and (4) eigenvector centrality—a measure of the influence of a node in a network (Tabassum et al., 2018) using the NetworkX (Hagberg, Swart & Chult, 2008) Python library.

CONCOR repeatedly partitions nodes into subsets based on structural equivalence and analyses Pearson’s correlations to search for groups with certain levels of similarity. It forms clusters, including nodes with similarities to each other (Breiger, Boorman & Arabie, 1975). CONCOR analysis was performed using the UCINET 6.0 software package (Borgatti, Everett & Freeman, 2002) for the analysis of social networks, and the clustering results were visualised using NetDraw.

Results

The frequencies of keywords related to adolescent stress

The frequencies of the top 30 words for adolescent stress in online news and blogs are shown in Table 1. The top five keywords were ‘counselling’, ‘school’, ‘suicide’, ‘depression’, and ‘activity’ in online news, and ‘diet’, ‘exercise’, ‘eat’, ‘health’, and ‘obesity’ in blogs.

Table 1:
Frequencies of 30 keywords related to adolescent stress in online news and blogs.
rank Word (news) Freq Word (blog) Freq
1 counseling 1143 diet 6553
2 school 682 exercise 3863
3 suicide 654 eat 2649
4 depression 629 health 2626
5 activity 619 obesity 2479
6 health 585 study 2007
7 education 509 counseling 1659
8 problem 499 treatment 1463
9 parent 454 sleep 1434
10 self-harm 440 problem 1382
11 mental health 423 parent 1379
12 treatment 415 skin 1354
13 family 403 weight 1351
14 study 401 help 1316
15 experience 398 intake 1241
16 participation 348 boxing 1232
17 smoking 335 acne 1113
18 friend 331 friend 1099
19 game 329 oneself 1077
20 oneself 305 person 1076
21 person 276 activity 1052
22 mind 260 mind 915
23 relieve 258 body 906
24 rest 242 rest 880
25 online 241 school 855
26 body 230 hair loss 844
27 anxiety 229 control 840
28 relationship 226 worry 801
29 worry 213 relieve 618
30 career 188 depression 589
DOI: 10.7717/peerj.15076/table-1

Analysis of centralities of keywords related to adolescent stress

Table 2 shows the centralities of keywords created using the keyword COM for online news. As the keyword ‘counselling’ was the most connected with three centralities, it had the highest degree of centrality, followed by ‘school’, ‘suicide’, ‘problem’, ‘self-harm’, and ‘depression’; the highest closeness centrality, followed by ‘school’, ‘suicide’, ‘problem’, ‘self-harm’, and ‘depression’; and the highest eigenvector centrality, followed by ‘school’, ‘suicide’, ‘problem’, ‘self-harm’, and ‘parent’. The keyword ‘suicide’ had the highest betweenness centrality, followed by ‘problem’, ‘counselling’, ‘school’, ‘activity’, and ‘depression’.

Table 2:
Centralities of keywords related to adolescent stress from news network.
Rank Keyword Cd Keyword Cb Keyword Cc Keyword Ce
1 counseling 0.897 suicide 0.061 counseling 0.906 counseling 0.260
2 school 0.897 problem 0.061 school 0.906 school 0.260
3 suicide 0.897 counseling 0.058 suicide 0.906 suicide 0.259
4 problem 0.862 school 0.058 problem 0.879 problem 0.253
5 self-harm 0.828 activity 0.043 self-harm 0.853 self-harm 0.251
6 depression 0.759 depression 0.040 depression 0.806 parent 0.237
7 activity 0.759 self-harm 0.036 activity 0.806 family 0.230
8 parent 0.759 health 0.034 parent 0.806 activity 0.230
9 health 0.724 parent 0.030 health 0.784 depression 0.230
10 mental health 0.724 mental health 0.029 mental health 0.784 mental health 0.228
11 family 0.724 education 0.023 family 0.784 health 0.225
12 education 0.690 family 0.022 education 0.763 education 0.214
13 oneself 0.552 treatment 0.011 oneself 0.690 oneself 0.197
14 experience 0.517 experience 0.007 experience 0.674 experience 0.185
15 treatment 0.483 oneself 0.003 treatment 0.659 study 0.176
16 study 0.483 study 0.003 study 0.659 friend 0.174
17 friend 0.483 friend 0.003 friend 0.659 treatment 0.161
18 person 0.414 person 0.000 person 0.630 relationship 0.156
19 relationship 0.414 relationship 0.000 relationship 0.630 person 0.154
20 mind 0.310 body 0.000 mind 0.592 mind 0.121
21 anxiety 0.310 game 0.000 anxiety 0.592 anxiety 0.118
22 participation 0.276 participation 0.000 participation 0.580 relieve 0.110
23 relieve 0.276 smoking 0.000 relieve 0.580 participation 0.108
24 game 0.241 mind 0.000 game 0.569 online 0.096
25 rest 0.241 relieve 0.000 rest 0.569 rest 0.094
26 online 0.241 rest 0.000 online 0.558 career 0.092
27 career 0.241 online 0.000 career 0.558 game 0.090
28 smoking 0.172 anxiety 0.000 body 0.537 worry 0.071
29 body 0.172 worry 0.000 worry 0.537 smoking 0.063
30 worry 0.172 career 0.000 smoking 0.527 body 0.060
DOI: 10.7717/peerj.15076/table-2

Notes:

Cd

Degree Centrality

Cb

Betweenness Centrality

Cc

Closeness Centrality

Ce

Eigenvector Centrality

Table 3 shows the centralities of keywords which were made by using the keyword COM for blogs. The keyword ‘diet’ had the highest degree of centrality, followed by ‘exercise’, ‘health’, ‘study’, ‘treatment’, and ‘problem’; the highest closeness centrality, followed by ‘exercise’, ‘health’, ‘study’, ‘treatment’, and ‘problem’. The keyword ‘treatment’ had the highest betweenness centrality, followed by ‘exercise’, ‘diet’, ‘study’, ‘health’, and ‘sleep’. The keyword ‘health’ had the highest eigenvector centrality, followed by ‘diet’, ‘exercise’, ‘problem’, ‘study’, and ‘parent’.

Table 3:
Centralities of keywords related to adolescent stress from blog network.
Rank Keyword Cd Keyword Cb Keyword Cc Keyword Ce
1 diet 0.862 treatment 0.068 diet 0.879 health 0.254
2 exercise 0.862 exercise 0.068 exercise 0.879 diet 0.254
3 health 0.862 diet 0.061 health 0.879 exercise 0.253
4 study 0.828 study 0.058 study 0.853 problem 0.247
5 treatment 0.793 health 0.057 treatment 0.829 study 0.245
6 problem 0.793 sleep 0.034 problem 0.829 parent 0.240
7 counseling 0.759 counseling 0.032 counseling 0.806 counseling 0.234
8 parent 0.759 help 0.027 parent 0.806 eat 0.234
9 eat 0.724 problem 0.026 eat 0.784 treatment 0.233
10 sleep 0.690 parent 0.021 sleep 0.763 obesity 0.216
11 obesity 0.655 eat 0.015 obesity 0.744 sleep 0.215
12 help 0.655 friend 0.014 help 0.744 help 0.209
13 oneself 0.586 activity 0.012 oneself 0.707 person 0.204
14 person 0.586 obesity 0.011 person 0.707 oneself 0.201
15 activity 0.586 oneself 0.007 activity 0.707 activity 0.198
16 friend 0.552 person 0.006 friend 0.690 friend 0.186
17 mind 0.448 skin 0.005 mind 0.644 mind 0.165
18 body 0.448 body 0.003 body 0.644 body 0.157
19 weight 0.414 worry 0.002 weight 0.630 school 0.143
20 school 0.379 weight 0.002 school 0.617 control 0.142
21 control 0.379 control 0.001 control 0.617 weight 0.140
22 rest 0.345 intake 0.000 rest 0.604 rest 0.133
23 intake 0.310 mind 0.000 intake 0.580 intake 0.107
24 worry 0.276 school 0.000 worry 0.580 worry 0.100
25 skin 0.241 boxing 0.000 skin 0.558 depression 0.089
26 depression 0.241 acne 0.000 depression 0.547 skin 0.073
27 acne 0.138 rest 0.000 acne 0.518 relieve 0.052
28 relieve 0.138 hair loss 0.000 hair loss 0.518 hair loss 0.041
29 boxing 0.103 relieve 0.000 relieve 0.518 acne 0.041
30 hair loss 0.103 depression 0.000 boxing 0.500 boxing 0.039
DOI: 10.7717/peerj.15076/table-3

Notes:

Cd

Degree Centrality

Cb

Betweenness Centrality

Cc

Closeness Centrality

Ce

Eigenvector Centrality

Semantic network of clusters through CONCOR analysis related to adolescent stress

Network groupings and visualisation of adolescent stress are shown in Figs. 2 and 3, respectively. Figure 2 shows the CONCOR analysis of the online news network of adolescent stress consisting of five clusters. We represented the cluster consisting of words 1, 2, … in [word 1, word 2, …]. The cluster [body, smoking, person, anxiety, mind, treatment, rest] could be considered ‘a pattern that occurs when adolescents are under stress’, as the cluster reflects having an anxious mind, searching for someone to be with, smoking, resting one’s body, or receiving treatment. The cluster [problem, parent, health, mental health, depression, experience, oneself] can be interpreted as ‘the causes and consequences of adolescent stress’, as adolescents’ experiences with problems between themselves and their parents affect their health and mental health, especially leading to depression. The cluster [worry, game, activity, relationship, friend] could be regarded as ‘a way for adolescents to relieve stress’, that is, to find a friend to relieve stress, talk about their worry, and play games or activities. The cluster [relieve, study, online, career, participation] could be interpreted as ‘to relieve the stress of studying, they participate in events such as online career experiences’. The cluster [suicide, self-harm, school, family, education, counselling] could be considered ‘education and counselling about self-harm or suicide due to stress is required at school and in the family’.

CONCOR analysis of news network of the adolescent stress.

Figure 2: CONCOR analysis of news network of the adolescent stress.

CONCOR analysis of blog network of the adolescent stress.

Figure 3: CONCOR analysis of blog network of the adolescent stress.

Figure 3 shows the CONCOR analysis of the blog network of adolescent stress, comprising seven clusters. The clusters [acne, skin, weight, intake] and [eat, obesity, health, diet, exercise, problem, parent] can be considered ‘sources of stress’; the cluster [school, rest, friend, boxing, relief] as in ‘coping with stress such as spending time with friends at school, resting, or boxing’; the cluster [study, counselling, activity] could be interpreted as ‘coping with the stress by studying, counselling, or activities’. The cluster [person, mind, depression, oneself] could be interpreted as ‘relationships between themselves and others causing stress and depression’, and the cluster [hair loss, control, body, worry] could be considered ‘a phenomenon that can occur when adolescents are under stress’ which were reflected in hair loss, (emotional) control, body (imbalance), and worry. The cluster [treatment, help, sleep] could be seen as ‘strategies adolescents can adopt to relieve stress’.

Discussion

Since the mental health of adolescents is extremely important for their future growth into healthy adults, various efforts are urgently needed to appropriately manage their current levels of stress. Therefore, in this study, we analysed online social big data on adolescent stress using text mining techniques.

As a result of text mining of keywords related to adolescent stress, the top five words with high frequency were ‘counselling’, ‘school’, ‘suicide’, ‘depression’, and ‘activity’ in online news, and ‘diet’, ‘exercise’, ‘eat’, ‘health’, and ‘obesity’ in blogs. The tendency of the top five words appeared such that the words of the blogs appeared somewhat lighter than those of the online news; this reflects the fact that the blogs were lighter, more personal, and informal, and these features distinguished them from the news (Tereszkiewicz, 2014). The fact that the top keywords of the blog were mainly related to diet and obesity reflects the high interest of adolescents in their bodies (Yun, 2018), which was also confirmed as a source of immense stress among adolescents.

Analysis of online news revealed that words that refer to resolving stress or behaviours caused by stress—such as counselling, school, self-harm, problem, and suicide—have high centrality. In contrast, in the centrality analysis of blogs, although treatment was highlighted, high centrality words reflecting the cause of stress—such as diet, study, and health—were also indicated. The result of ‘counselling’ having the highest connection with other keywords in network centrality analysis of the online news may indicate a lot of resolution in consideration of the social issues of adolescent stress due to the nature of news which has a formal character (Jeong & Kim, 2010). As the characteristics of blogs are considered temporary, personal, and informal (Thorsen & Jackson, 2018), it was confirmed that adolescents frequently shared personal causes of stress through individual blogs. Furthermore, since social media can influence adolescents’ self-views and interpersonal relationships through social comparisons and negative interactions, social media content often promotes self-harm and suicidal thoughts among adolescents (Abi-Jaoude, Naylor & Pignatiello, 2020). Corroborating the descriptions in this last cited study, another study reported that adolescents with depression and/or suicidality often use more social media and report worsening mood and suicide risk (John et al., 2018). Nonetheless, researchers also found that lower levels of social media use (overall and messaging) are associated with a greater likelihood of having suicidal ideation with plan over the next 30 days (Hamilton et al., 2021). Therefore, it is relevant to consider the importance of social media as an additional context for the topic of adolescent suicide and to educate adolescents to avoid placing indiscriminate trust on social media.

Through CONCOR analysis, five clusters were identified in online news and seven in blogs. The causes and symptoms of stress and coping strategies were confirmed in both online news and blogs. However, online news contained multiple coping strategies for relieving stress, whereas blogs focused more on the causes of stress. ‘Study’ as a cause of stress was included in both online news and blogs. Diet, obesity, acne, skin, hair loss, and various other causes of stress, such as school, and family, have been included in blogs and highlighted in previous studies. Contrarily, online news focused more on coping strategies for relieving adolescent stress. The importance of counselling for adolescent concerns, education, and counselling in schools and families was also confirmed. Korean adolescents are under a lot of stress due to their studies, and with excessive academic stress, they may experience mental health-related problems, such as depression (Kang, 2022). Because modern society tends to judge and evaluate people based on their appearance, it is common for adolescents to rate themselves based on their appearance. Acne occurrence is associated with stress and depression; therefore, acne treatment and skin care are considered necessary for improving mental health (Shin & Kim, 2019).

Our analyses indicate that blogs contain more content about the causes and symptoms of stress than online news, reflecting the trend of social networks of casual and informal youth blogging as a new channel for sharing personal information (Li et al., 2016). In one study, researchers designed and implemented a microblogging platform to detect and relieve stress in teenagers; the authors mentioned the potential for stress detection because stressed individuals view microblogs as a channel for emotional release and interaction (Zhao et al., 2016). Young people who experience illnesses, including stress, tend to blog about them, and such blogs often have many followers. By means of blogging, young people living with an illness may succeed in having a social life and uphold and even extend their self-knowledge and self-esteem. The content of a blog can foster familiarity between the author and readers. Blogs can provide the authors’ unique experience-based knowledge and reflection to readers who read published articles. Therefore, blogging, especially on specific issues involving stress, should be continuously explored and recognised as a valuable source for such content in the future (Nesby & Salamonsen, 2016). This finding suggests that differences in blog authors’ subjective thoughts and direct experiences are used as the main basis for blogs, whereas online news focuses on delivering objective information and explanations based on the values of fairness and responsibility (Jeong & Kim, 2010). Nowadays, Internet usage is unavoidable for the younger generations. The online world is the primary source of information and quick communication; therefore, education about the correct use of the internet should be made reasonable at the earliest (Prievara & Piko, 2016).

Finally, the results suggest that when establishing a stress coping strategy for adolescents, first, the information currently present in social media can be utilised by stakeholders; this is in consideration that blogs focused more on the causes of stress and online news contain relatively large amounts of information on stress coping. Second, since elements related to appearance and academics are often cited as causes of stress for Korean adolescents, they should be incorporated into stress coping interventions aimed at this population. Third, social media could be given greater importance within the context of adolescent suicide.

Conclusions

To contribute to a strategy for preventing and managing adolescent stress, we analysed social media data using text-mining techniques and derived the words and word associations from online news and blog content. We collected data from the two largest portals for Koreans, including Korean adolescents, using the search term ‘Adolescent stress’ and related words. In this study, we collected only data that is in Korean from Korean portal sites. Although information about adolescent stress is available on websites worldwide, only data in Korean language were collected to ensure consistency with keyword selection. Despite these limitations, the results of this study are valuable as they were derived through social big data analysis of data obtained from online news and blogs. The findings provide a wide range of implications related to adolescent stress; hence this study can contribute as basic data for the stress management of adolescents and their mental health management in the future.

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