Relationship between unemployment, depression, and suicide during COVID-19 among males in South Korea: based on internet search trends

1. Introduction

In 2020, the COVID-19 pandemic triggered a profound global public health crisis. The shutdowns and social distancing measures implemented to curb virus transmission led to a simultaneous slowdown in labor market demand and supply, resulting in unprecedented levels of unemployment. This situation brought attention to emerging challenges such as the increase in remote work, declining productivity, and rising wage inequality [1, 2]. The increase in stay-at-home measures and remote work due to COVID-19-related social distancing measures, along with a decrease in Gross Domestic Product (GDP) [3], financial insecurity, unemployment, and pervasive pandemic-related anxiety have significantly impacted the mental health of the population [4]. According to the International Labour Organization (ILO), working-hour losses in 2020 were approximately four times greater than those during the global financial crisis of 2009 [5]. Working-age individuals who faced acute work loss and unemployment, as well as financial distress during the COVID-19 pandemic, were more likely to report psychological distress and poor mental and physical health [6, 7]. Notably, during the economic crisis of 1997–1998, East and Southeast Asia countries experienced a rise in suicide rates, particularly among males, which was closely linked to unemployment experiences [8]. Due to COVID-19, workplaces experienced fears of infection and uncertain economic recovery [9], resulting in increased job insecurity in industries and lower job satisfaction [10]. A study examining the relationship between financial concerns and job insecurity during COVID-19 found that [11], greater job insecurity due to COVID-19 was related to greater depressive symptoms, and greater job insecurity was indirectly related to greater anxiety due to financial concerns. Under the COVID-19 situation, job instability and job insecurity have shown a significant negative impact on workers’ work satisfaction [12].

The COVID-19 pandemic, a study examining the association between losing work and mental and physical health status classified individuals who experienced work loss into three groups: the reduced work group, off-work group and lost job group. These groups were compared with the unaffected work group, which consisted of individuals who did not experience work loss. The results indicated that individuals in the work loss groups had significantly higher odds of experiencing severe psychological distress, poor mental health, and poor physical health compared to those in the unaffected work group. Specifically, it was reported that among the lost job group, individuals with fewer financial resources were 8.4 times more likely to experience psychological distress than those in the unaffected work group [6]. Financial difficulties related to COVID-19 were also found to predict suicidal thoughts or behaviors through depression and feelings of loneliness [13]. The global economic crisis has significantly impacted suicide rates in European and American countries, particularly among men and in regions where the hardest job losses occurred [14]. Longitudinal studies conducted across various countries have identified economic crises and unemployment as significant risk factors for increased suicide mortality and suicidal behavior [15, 16]. A study analyzing the impact of unemployment on suicide mortality in Western European countries from 2000 to 2010 found a significant 0.3% overall increase in suicide rate for a 10% increase in unemployment rate [17]. Unemployment resulting from economic crises has been shown to have a significant relationship with suicide both in the short term and the long term [18]. A study spanning 18 years (2000–2017), and investigating the predictors of suicide rates in 38 Organization for Economic Co-operation and Development (OECD) countries found that unemployment had the most substantial impact on increased suicides, especially among middle-aged groups [19]. In addition, stress related to COVID-19, economic hardship, and social isolation were found to have a significant relationship with suicidal thoughts, self-harm and suicide [20, 21]. In the case of South Korea, which consistently had the highest suicide rates among the OECD countries, an unusual trend emerged in 2020, while the overall suicide rate decreased due to various suicide prevention policies, the suicide rate among men increased by 30% compared to 2017 [22]. Furthermore, depression—a major predictor of suicide—has increased in all major OECD countries since the onset of the COVID-19 pandemic [4]. Notably, South Korea ranked first (36.8%) among OECD countries, well above the mean depression level (21.8%), with a higher prevalence among women than men [23]. As such, previous empirical studies have demonstrated significant links between financial crises, job loss, job insecurity and wage reductions, and elevated rates of depression and suicide.

Recently, research using Internet search data analysis—particularly from platforms like Google—has increased as a tool for identifying and addressing public mental health issues like depression, loneliness and suicide [24]. During COVID-19, the use of free and easily accessible tools such as Google Trends data has become more prevalent for measuring changes in people’s mental health and public concerns [25, 26]. By utilizing machine learning techniques to classify suicide risk in large samples, it was revealed that depression symptoms were the most relevant for the classification of increased suicide risk [27]. According to a 2021 survey of all South Koreans over the age of 3, 91.9% of South Koreans use the Internet, primarily for searching for data and information [28]. The demand for obtaining health and medical information through online searches has increased significantly due to the prolonged pandemic. However, the Internet contains both beneficial and harmful information, and search engines play a critical role in moderating access to suicide-related content. In other words, these platforms can inadvertently expose users to harmful information that may significantly influence suicidal thoughts and behaviors [29]. Studies show that the volume of Google searches for suicide-related terms has shown a positive correlation with suicide rates [30]. Additionally, a study by Fond et al. [31] found that the frequency of searches for “suicide” is associated with searches related to depression, indicating that search engine data could be a critical indicator for suicide prevention efforts. Thus, utilizing internet search engines like Google is increasingly recognized as effective for monitoring suicide risk, with Google search data providing a more accurate estimate of suicide rates than traditional self-reported assessments [31, 32]. Furthermore, a study on internet search trends in Taiwan identified significant associations between suicide deaths and various suicide-related search terms [33].

When conceptualizing suicidal ideation, thoughts, and attempts along a continuum of suicidality, traditional questionnaire-based methods for assessing perceptions of suicide risk may encounter issues, such as cognitive dissonance during the interview process. As a result, analyzing internet search trends for suicide-related terms has emerged as a valuable alternative [34]. Traditional quantitative research methods often struggle to capture the full scope of social phenomena [35, 36]. However, an approach of using Internet searches allows researchers to examine “collective browsing behavior” offering insight into “collective thinking”, that reflects the shared concerns and behaviors of a population [37]. Research suggests that individuals at risk of suicide are more likely to seek out websites that provide suicide-related information [38]. Therefore, in countries like South Korea, where there is a high volume of online search activity related to suicide, leveraging this data could be a practical way to identify individuals at risk [39].

The use of Internet search terms improves the predictive power of research models. Several studies have identified a significant association between the search volumes for keywords like “depression” and “suicide” and “commit suicide” on search engines and the actual suicide rates [24, 26, 30, 39]. In addition, a validated suicide prediction model links Internet searches related to suicides with actual suicide rates, further supporting the reliability of this approach [40]. Yang et al. [33] analyzed the association of Internet search trends with suicide death in Taipei city, between 2004 and 2009. They employed multiple linear regression analysis and found a positive correlation between the suicide-related search terms and actual suicide rates.

Research examining the relationship between internet searches and suicide has found a strong correlation between search terms such as “commit suicide”, “commit suicide without pain” and “suicide”, as well as the topic “suicide”, exhibited a high correlation with actual suicide rates [41]. Monitoring trends through internet search engines such as Google can effectively monitor suicides, assess risks early, and serve as an effective tool for suicide prevention interventions. This framework supports the development of a predictive model aimed at preventing future suicides by utilizing trends in unemployment-, depression- and suicide-related searches during the COVID-19 pandemic.

With increased access to search volume data, a growing number of studies are exploring correlations between online search volume with other variables, assessing whether search volume can predict social phenomena. Similar to these studies, Internet search trends, such as Google Trends, are also utilized as tools for predicting and managing various issues in mental health and health care domains [24, 25, 27, 40, 42]. In particular, big data generated from internet searches offers valuable insights into health behaviors. Consequently, studies analyzing keywords through search engines like Google Trends in medical research are rapidly increasing [43, 44].

Accordingly, this study aimed to develop a predictive model of suicide risk based on online search trends (or online search volume) related to unemployment, depression and suicide during the COVID-19 pandemic. For this purpose, we used data obtained from Naver, which is Korea’s most commonly used search engine [28]. Subsequently, we examined the mediating effect of the depression-related search volume on the relationship between unemployment- and suicide-related search volumes. By understanding these dynamics, we aimed to explore interventions for effective suicide prevention.

2. Material and methods

2.2 Research model

To determine the structural relationship between the search volumes of the keywords “unemployment”, “depression” and “suicide”, Hayes’ SPSS PROCESS Model 4 (4.2 version, Andrew F. Hayes, Calgary, AB, Canada) [48] was applied. The research model is presented in Fig. 1.

Fig. 1.Research model.

3. Results

3.1 Descriptive statistics and correlations of the main variables

The descriptive statistical analysis of the main variables showed that the average search volume for unemployment was 3.31 points (Standard Deviation (SD) = 1.02), while depression was 5.34 points (SD = 1.04) (Table 1). The average search volume for suicide was found to be 11.20 points (SD = 4.51). In other words, the search volume was highest for suicide, followed by depression and then unemployment.

Table 1.Descriptive statistics.

Variable	Min	Max	Mean	SD	Skewness	Kurtosis
Unemployment search volume	1.01	8.35	3.31	1.02	0.286	0.417
Depression search volume	3.24	13.84	5.34	1.04	1.489	2.581
Suicide search volume	6.49	100.00	11.20	4.51	1.989	3.669
Note: SD: Standard Deviation; Min: Minimum; Max: Maximum.

The normality of each variable was assessed, and the skewness did not exceed an absolute value of 2, while kurtosis did not exceed an absolute value of 4. Based on Kline’s criteria [50], these values indicate that the variables are normally distributed.

Fig. 2 shows the relative change in search volume over the analyzed period by aggregating the search volumes for each keyword and setting the maximum search volume to 100. The relative search popularity of unemployment, depression, and suicide revealed that suicide had the highest search volume, followed by depression and then unemployment. Regarding the temporal changes in the search volumes for each keyword, excluding certain dates with significantly high search volumes, unemployment and depression generally maintained a stable, average level. However, searches related to suicide did not exhibit regular patterns, but there were numerous dates with a sharp increase in search volume.

Fig. 2.Relative search volume over the analyzed period among male. Note: Search volume: The date with the highest search volume is set at 100 points, and the search volume for other dates is quantified within a range of 0 to 100 points according to the proportion.

To examine the correlations among the search volumes for unemployment, depression, and suicide, a correlation analysis was conducted. The correlations between unemployment and depression (r = 0.463, p < 0.001), depression and suicide (r = 0.543, p < 0.001) and unemployment and suicide (r = 0.235, p < 0.001) were all positively significant at the p < 0.001 level. A correlation coefficient above 0.8 among independent variables is generally indicative of multicollinearity risk. The analysis revealed that none of the independent variables had a correlation coefficient exceeding 0.8, indicating no multicollinearity concerns. Additionally, the Variance Inflation Factor (VIF) values were assessed to confirm multicollinearity risk, and all values were found to be slightly above 1, well below the threshold of 10, confirming that multicollinearity was not an issue.

4. Discussion

This study examined the mediating effect of depression on the relationship between online search volume related to unemployment and suicide. It was found that depression-related search volume mediates the relationship between the unemployment- and suicide-related search volumes. Specifically, the unemployment-related search volume influenced the increase in the depression-related search volume, which, in turn, affected the increase in the suicide-related search volume. These results are consistent with previous studies that have reported a statistically significant relationship between depression-related and suicide-related search volumes, as well as between the search volumes of actual suicide rates and the number of suicide victims [26, 38, 51, 52]. Furthermore, our findings align with studies that have identified the global economic crisis resulting from COVID-19 as a factor leading to wage reductions, low income, job insecurity, job instability [12], unemployment, economic insecurity and financial strains. The unemployment of workers resulting from such economic crises serves as a risk factor for depression and suicidal behavior [16, 18, 20, 21].

Although we did not analyze the correlation between actual suicide rates and the number of suicide victims, our findings may help in predicting suicide risk. Previous studies that have shown that unemployment negatively affects people’s mental health and increases the risk of suicide during the national economic crisis. During COVID-19, job insecurity, financial crises, and job instability have been found to impact workers’ mental health, leading to anxiety, depression, and suicidal tendencies. Our research results indicate that men’s employment is associated with depression, supporting the findings of Wilson et al. [11], which suggest that job insecurity and financial concerns influence depression and anxiety, as well as previous studies indicating correlations between working-hour losses, job insecurity, unemployment, and depression during COVID-19 [12, 16, 17].

Particularly, the findings regarding the association between unemployment, depression, and suicide among Korean men corroborate the study by Chang et al. [8], which investigated the economic crisis and suicide rates in Japan, Hong Kong, Korea, Taiwan, Singapore and Thailand, showing that the economic crisis had the strongest impact on suicide rates among working-age men. Consequently, it is possible to infer an increased risk of depression and suicide during the socioeconomic crisis caused by the COVID-19 pandemic. Our findings support previous studies indicating that unemployment mainly affected the mental health and suicide rates among men during previous financial crises [11, 19].

Moreover, these search volumes ultimately affected the suicide-related search volume. Although the relationship between the findings and actual suicide rates has not been demonstrated, they can be considered a reflection of the increased suicide rates among South Korean men during the pandemic. Contrary to previous studies, where the unemployment of men (typically the primary breadwinners) was highly correlated with depression, the COVID-19 pandemic created a social situation characterized by disproportionately high employment instability and disruptions in face-to-face services, including sales, healthcare and education [5].

Based on these findings, suicide prevention policy strategies should not be limited to mental health promotion measures alone. Instead, the government should establish dual-faceted policies that provide support to economically disadvantaged individuals. In addition to economic factors, such as loss of income, unemployment negatively impacts self-perception, social identity, and self-esteem. Therefore, it is necessary to enhance the comprehensiveness and continuity of national policies to simultaneously provide economic and mental health support in cases of job loss or unemployment. Currently, suicide prevention programs in South Korea are primarily run by mental health welfare centers, including a small number of suicide prevention centers, but there are limitations in reaching people experiencing economic hardship. It will be necessary to identify government agencies with integrated services to provide support and counseling for both unemployment and mental health, especially for women.

Furthermore, considering the significant correlations between depression- and suicide-related search volumes found in this study and previous literature, as well as the actual suicide rates and the number of suicide victims, it is necessary to establish a web-based suicide prevention system by effectively responding to Internet search trends. In particular, suicide-related online search activities indicate suicide risk. In Spain, internet searches for suicide-related terms are positively related to actual suicide rates [26], and the study by Barros et al. [53] also found that search terms such as “suicidal”, “suicide methods” and “how to commit suicide” are significantly correlated with suicide rates. Similarly, Yang et al. [33] reported that an analysis of 37 suicide-related search terms revealed a relationship between these search terms and actual suicide rates. Previous studies have demonstrated a significant association between suicide searches and suicide rates [26, 29]. This study’s findings suggest that Internet search trends can be utilized as predictive tools to identify individuals with suicidal intentions and facilitate relatively easy access for prevention and management opportunities [31, 53]. Additionally, the research by Chandler et al. [46] indicates the potential of these trends to serve an online gatekeeping role in suicide prevention. Furthermore, it highlights the utility of monitoring suicide-related searches as a tool for tracking and identifying individuals at risk of depression and suicide [37]. These insights further align with existing literature that underscores the importance of understanding online behaviors related to suicide in order to address public mental health issues effectively [36, 52].

Setting up an online database to recognize accompanying signs and preemptively monitor and intervene could be effective. Additionally, it is important to identify various related search terms that can predict suicide risk, given the close relationship between depression-related keywords search volume and suicide-related search volume. By applying and utilizing Information and Communication Technology (ICT)-based services in mental health, as required by the advent of the fourth industrial revolution and the COVID-19 pandemic, this could be a valuable preventive approach. This study is significant as it developed a suicide risk prediction model using behavioral data obtained from the Internet prior to suicide, and we sought relevant alternative measures. In addition, this study addressed the limitations of existing research by focusing on the internet aspects of methodology, reflecting the situation of COVID-19 through big data analysis.

5. Limitation

The limitations of this study and the suggestions for future research are as follows. First, while we used Internet search volume by date of COVID-19 as the main variables in the form of time series data, this approach is limited in its ability to reflect the complex psycho-emotional factors interacting with suicide risk, such as demographic characteristics, socioeconomic circumstances (e.g., social capital, duration of unemployment, number of people in the workforce, decline in real wages), and individual mental health vulnerabilities. Second, in this study quantified the search volume was quantified within a range of 0 to 100 points, with the date having the highest search volume set at 100 points, and other dates adjusted accordingly. This method presents a limitation, as the analysis was based on relative search volume rather than exact figures. Therefore, it is recommended that future research use more objective figures if accurate search volume data can be extracted. Third, while using the date as the unit of analysis may be seen as a creative and novel approach from an academic perspective, it poses challenges in analyzing the context of specific time points or setting hypotheses. Future studies should examine changes in search volume for terms like suicide over time and analyze the causes behind these trends. Furthermore, given that our study was limited to the period of the COVID-19 pandemic, future studies should examine the relationship between unemployment, depression, and suicide longitudinally by comparing the pre- and post-COVID-19 periods. Additionally, caution is needed when interpreting the results, as we did not examine actual suicide rates or the number of suicide victims. Thus, introducing official statistical data (e.g., the number of suicide victims per date) into the prediction model and analyzing the relationship between the search volumes and actual suicides in future studies will provide more sophisticated, generalized results and further facilitate the discussion on effective intervention methods.

6. Conclusions

In modern times, Internet trends reflect the character of a particular society. In this respect, the online search trends for specific keywords also depict societal realities. This study highlights the strength of using Internet search trends to investigate the relationship between unemployment, depression, and suicide data among Korean males, particularly during the COVID-19 pandemic. For instance, online search activities using suicide-related keywords indicate a heightened suicide risk. By using Internet search trend data and considering the pandemic-driven situation, we verified the correlation volumes of terms related to unemployment, depression, and suicide during the COVID-19 pandemic. This correlation not only underscores the mental health implications of economic instability but also highlights the urgent need for targeted interventions. Although the predictive model is based on online keyword searches, the mediating effect of depression on the relationship between unemployment and suicide reflects the prevailing social structure of the time. These findings suggest that economic factors play a significant role in mental health, particularly in crisis situations, highlighting the need for comprehensive support systems. Based on these results, we suggest the possibility of establishing a web-based suicide prevention system that monitors and utilizes the online search trends of terms that are significantly associated with suicide risk. This proactive approach could facilitate timely interventions and support for individuals experiencing heightened vulnerability due to economic and mental health challenges.

Availability of data and materials

The datasets used in the study are available from the corresponding author upon request.

Author contributions

MY, JY, KJ and HJC—designed and conducted the research study. MY and KJ—collected and analyzed the data; interpreted the data; drafted the manuscript. HJC—revised the manuscript’s content. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board (IRB) of Jeonbuk National University (JBNU 2022-08-012), and the study procedures were undertaken in accordance with the Declaration of Helsinki.

Acknowledgment

Not applicable.

Funding

This paper was supported by international research funds for humanities and social science of Jeonbuk National University in 2022.

Conflict of interest

The authors declare no conflict of interest.