Semantic Network Analysis of Newspaper Articles related to Agro-healing

Article information

J. People Plants Environ. 2022;25(2):167-176
Publication date (electronic) : 2022 April 30
doi : https://doi.org/10.11628/ksppe.2022.25.2.167
1Ph.D. Candidate, Department of Plant Resources, Gyeongsang National University, Jinju 52725, Republic of Korea
2Professor, Department of Plant Resources, Gyeongsang National University, Jinju 52725, Republic of Korea
3Professor, Department of Plant & Biomaterials Science, Gyeongsang National University, Jinju 52725, Republic of Korea
4Institute of Agriculture & Life Science, Gyeongsang National University, Jinju 52828, Republic of Korea
*Corresponding author: Yong-Wook Shin, ywsynn@gnu.ac.kr, https://orcid.org/0000-0002-4983-0107
Received 2022 March 7; Revised 2022 April 10; Accepted 2022 April 15.

Abstract

Background and objective

Despite the fact that the COVID-19 pandemic has increased the demand for mental health services, access has been limited, resulting in service gaps and severance. Agro-healing, which is expected to be utilized successfully to promote mental health for both individuals and communities, could be a solution. This study was conducted to provide basic data for revitalizing policies and research related to agro-healing by analyzing the trends in big data of online news articles over the last decade.

Methods

A total of 2,310 news articles related to agro-healing were collected from January 1, 2012 to December 31, 2021 by crawling Naver News. To extract nouns with practical meaning, the Okt morphological analysis of the KoNLPy module in Python 3.9 was employed. Semantic network analysis was conducted to validate degree centrality, betweenness centrality, and eigenvector centrality in order to understand the centrality and connectivity of significant keywords. The data was visualized using Gephi 0.9.2 by performing CONCOR analysis to generate clusters.

Results

The keywords with the highest degree centrality were agro-healing, followed by healing, care farm, vitality, RDA, citizens, and rural tourism. Agro-healing, Healing, stress, urban, disabilities, care farm, dementia, and rural area were highest in terms of betweenness centrality. The eigenvector centrality was highest in agro-healing, followed by vitality, healing, care farm, and effect. As a result of the CONCOR analysis, four clusters were identified: ‘agro-healing characteristics’, ‘agro-healing resources’, ‘agro-healing activities’, and ‘agro-healing target and effect’.

Conclusion

According to the findings, social expectations and need for agro-healing to improve public health became a significant part of the discourses. This research is expected to help determine future research and policy directions, as the vitality of agro-healing continues to provide national welfare services and seek sustainable growth in agricultural and rural areas.

Introduction

The prolonged COVID-19 pandemic is increasing the demand for mental health services while also bringing confusion to accessing and providing services. According to the mental health services survey on 130 countries by the WHO (2020), the ongoing COVID-19 has increased the sense isolation, depression, anxiety, and insomnia, and the concern or fear about income decrease and the pandemic itself led to severe mental health problems, and thus 89% of the member states felt the need for mental health services. However, insufficient experts and infrastructures or concerns about infection due to face-to-face services resulted in lower service accessibility, causing the service gap and severance among not only the users that had been using mental health services before but also general citizens suffering from direct or indirect psychological trauma due to the COVID-19 pandemic (Yun, 2020; Park, 2020). Under these circumstances, agro-healing (or healing agriculture) that refers to activities and industries promoting physical, emotional, mental, cognitive, and social health of citizens using agriculture or rural resources (RDA, 2016) can be the solution.

Jang et al. (2019) discovered that agro-healing activities had a positive effect on the emotional aspect of the elderly such as psychological stability by lowering their stress and blood pressure. Cacciatore et al. (2020) stated that programs using the nature and animals of care farms help emotionally heal people who experienced emotional trauma, and thus can be an alternative to antipsychotic medications. Moreover, agro-healing activities have a great impact on mental well-being by increasing life satisfaction, positive emotions, and self-esteem and reducing loneliness, anxiety, and depression (Greenleaf and Roessger, 2017; Hine et al., 2008; Hemingway et al., 2016). They are also free of negative stigma about sanatoriums, rehabilitation centers, and medical institutions related to mental health (Elings, 2012), thereby showing potential in attracting active participation and smoothly providing services.

As such, agro-healing is proved to be an effective measure to promote mental health for individuals and society. Thus, the Netherlands, the United Kingdom, and the United States are actively implementing and studying agro-healing programs that systematically linked agricultural production, public health, and social services (Hassink et al., 2014; Bragg, 2013; Artz and Davis, 2017). With the growing interest and need for agro-healing that followed the enforcement of the Act on Research, Development, and Promotion of Healing Agriculture in 2021, South Korea has been mostly conducting basic studies for development and promotion of agro-healing, such as analysis of care farm programs (Yoo et al., 2021), qualitative research on agro-healing in social welfare (Lee et al., 2020), and priority analysis on the agro-healing classification system (Yoo et al., 2021).

However, despite the social interest and importance, there is insufficient research that identified the public awareness and trends about agro-healing using big data. In particular, considering how each local government establishes ordinances related to agro-healing and conducting individual studies to find ways to expand and promote agro-healing, it is necessary to identify the discourse on agro-healing that has become a social issue. Therefore, the purpose of this study is to analyze the trends in articles about agro- healing over the past 10 years and provide basic data to activate policies and research related to agro-healing.

Research Methods

Data extraction and purification

Although the Act on Research, Development, and Promotion of Healing Agriculture has been enforced in March 2021, the social discourse has begun to be formed in 2012 when agro-healing was selected as a keyword to focus on regarding change in the agro-industry (RDA, 2012). Thus, the data collection period was set as 10 years starting from 2012. For the data collection platform, this study used Naver News that has the biggest number of users among web portals and provides news data of major media companies at once (Park and Shin, 2021).

First, to collect news articles using ‘agro-healing’ as the keyword, BeautifulSoup and SeleniumLibrary of Python 3.9 (Pycon, USA) were used to crawl Naver News of 10 years from January 1, 2012 to December 31, 2021. After eliminating redundant data, total 2,310 news articles were selected for final analysis.

Analysis method

It is more effective to tokenize and embed Korean words in morphemes rather than tokenizing them based on spaces like English words (Kang and Yang, 2018). To analyze the keywords of the finally selected news articles, Okt morphological analysis of Python KoNLPy that is widely used in natural language processing was used for tokenization that extracts nouns with actual meanings. Moreover, compound nouns were identified to analyze the key related words and designated as a single word, and stop words like special characters were eliminated.

Then, to identify the trend in the newspaper article discourse, NetworkX of Python was used to conduct a semantic network analysis that can analyze the semantic pattern of messages through the relationship between words used simultaneously within one sentence and the frequency of words (Shi et al., 2016). Semantic network analysis is a useful methodology in identifying the flow of intentional meanings of the text in news articles (Kim, 2017a). This study used the CONvergence of iterated CORrelations (CONCOR) to identify the relationship between clusters using centrality analysis and UCINET 6 (Analytic Technologies Corp, USA). The results were visualized using Gephi 0.9.2 (USA) (Fig. 1).

Fig. 1

Data analysis flowchart.

Results and Discussion

Analysis of related keywords in agro-healing news articles

From 2012 to 2015, there were around 60 articles related to agro-healing every year, maintaining a similar level, but the number doubled in 2016 and has since constantly increased. In particular, the heatmap analysis in Fig. 2 shows the rapid increase in the number of articles since 2020 when the Act on Research, Development, and Promotion of Healing Agriculture was enforced and the WHO announced the COVID-19 pandemic. A heatmap is a useful chart for data visualization when there are many variables or subjects of comparison since the high and low values can be identified at a glance using different shades (Ryu and Song, 2014).

Fig. 2

Heatmap of articles mentioning agro-healing by month.

Table 1 shows the results of analyzing keywords in 2,310 news articles on agro-healing from 2012 to 2021. The top 30 among 436 keywords derived based on words with at least two characters and at least 30 times of co-occurrence were: agro-healing (2,071), rural area (1,573), program (1,489), care farms (1,485), education (1,456), support (1,355), healing (1,307), operation (1,276), urban (1,262), and society (1,253). These words well display the characteristics of the discourse on agro-healing, through which it was possible to identify the trend in the social perception of agro-healing such as operating and supporting agro-healing activities including various programs and education using agro-healing resources like rural areas and care farms.

Top 30 frequent keywords of agro-healing in online news articles

The top 5 keywords in each year were as follows. In 2012, they were rural area (64), healing (43), society (42), health (39), and environment (35), and in 2013, they were support (60), vegetable garden (58), healing (45), program (41), and village (39). In 2014, they were healing (188), industry (179), targets (148), plants (135), and education (75), and in 2015, they were care farms (259), rural area (151), urban (122), operation (102), and healing (84). In 2016, they were healing (291), education (249), citizen (230), rural tourism (220), and resources (216).

In 2017, the top 5 keywords were healing (326), rural area (260), vitality (247), program (199), and rural tourism (176). In 2018, they were education (345), care farms (341), welfare (324), future (327), and healing (315), while in 2019, they were development (421), care farms (416), agro-healing (402), effect (377), and welfare (298). In 2020, they were support (585), agro-healing (506), dementia (421), RDA (413), and COVID-19 (372). In 2021, they were agro-healing (562), dementia (508), stress (466), society (421), and COVID-19 (403) (Table 2).

Top 5 frequent keywords of agro-healing in online news articles by year

As agro-healing emerged as a keyword in agricultural and industrial changes with the words above, agro-healing activities began to receive attention, proving people’s interest and expectations for community development through agro-healing and welfare services that are applicable to various targets. This is in line with the results of previous studies proving that agro-healing leads to preventive, therapeutic, and rehabilitation effects in physical, cognitive, psychological, and social aspects of humans throughout the entire life (Jang et al., 2021) and that it can be used positively to reduce various problems in the modern society (Jang et al., 2019). In particular, considering that COVID-19 pandemic is one of the main keywords of 2020 and 2021, agro-healing is receiving more attention as an activity for emotional support that relieves depression and stress due to COVID-19 pandemic.

Semantic network analysis

Semantic network analysis is an analytical method using social network analysis that is used to identify the flow of intentional meaning in the text of newspaper articles (Cha, 2015; Choi and Kweon, 2014). This study examined degree centrality, betweenness centrality, and eigenvector centrality in the centrality analysis of semantic network analysis to identify the centrality and connectivity of the top 50 keywords (Table 3).

Semantic network analysis of top 50 related words

For degree centrality of the keywords, agro-healing was highest at 1.0, followed by healing 0.95, care farms 0.94, vitality 0.93, RDA 0.90, citizen 0.90, and rural tourism 0.90. Degree centrality measures how many edges one node has attached to other nodes in the network, and the higher the degree, the more central the node is (Kim, 2017b). The words with high degree centrality at the top implied that agro-healing led by the RDA can bring vitality to rural areas using rural tourism and care farms. Moreover, words such as citizen, education, rural area, farmhouse, and program showed the public interest in agro-healing education and service as well as the direction for agro-healing.

For betweenness centrality, agro-healing was highest at 0.75, followed by healing 0.69, and then stress, urban, disabilities, care farms, dementia, and rural area showing the same centrality at 0.67. Compared to degree centrality, words such as stress, disabilities, and dementia were at the top, having a relatively great impact on the network. Nodes with high betweenness centrality have great control over the flow of knowledge and information exchange (Kho et al., 2013; Park and Kwahk, 2013). This indicates that agro-healing is widespread among not only general citizens in need of stress management, but also various members of the society such as persons with disabilities or dementia. Words like welfare, field, patient, and experience show that they affect participation and promotion of agro-healing associated with social services.

For eigenvector centrality, agro-healing was highest at 0.98, followed by vitality 0.86, healing 0.81, care farms 0.81, and effect 0.78. Words at the top except effect all matched the four words at the top for degree centrality. Eigenvector centrality shows the words that are at the centermost of the entire structure, considering not only the nodes that are directly and indirectly connected but also the centrality of the other nodes that are connected (Kim and Ahn, 2012; Hong and Yun, 2014). Thus, the word effect along with vitality, healing, and care farms with high eigenvector centrality has high connectivity with agro-healing as well as a great impact, thereby serving as a keyword that is the center of the social discourse formation and development.

The results above are visualized with Gephi 0.9.2 after identifying the clusters through CONCOR analysis (Fig. 3). CONCOR is a method identifying the clusters of words and determining the relationship among the clusters by repeatedly analyzing Pearson’s correlation among words and finding an adequate level of similarity groups (Eum and Leem, 2021: Kim and Jun, 2014). The results showed that there were total 4 clusters, each of which was named agrohealing characteristics, agro-healing resources, agro-healing activities, and agro-healing target and effect.

Fig. 3

Visualization with CONCOR analysis.

First, the ‘agro-healing characteristics’ group comprised of words such as vitality, healing, care farms, RDA, program, education, and service represents agro-healing itself, which aims to regain the vitality of rural areas and activate agro-healing resources and activities, thereby promoting mental, social, cognitive, and physical health of citizens (Kim et al., 2013). Second, the ‘agro-healing resources’ group comprised of words such as rural area, rural tourism, village, expert, farmhouse, field, and university includes words that represent resources used in agro-healing such as venue or human resources for relevant activities. These words showed that the value and role of rural areas are emphasized as a healing space, and proved the importance and demand for experts and operating agencies.

Next, the ‘agro-healing activities’ group comprised of words such as vegetable garden, cultivation, horticulture, nature, environment, future, and support showed the aim for sustainable growth of agriculture and rural areas through agro-healing. Moreover, agro-healing activities such as vegetable gardens and horticultural activities are used as a means to recover health and improve the quality of life. Finally, the ‘agro-healing target and effect’ group comprised of words such as disabilities, dementia, citizen, stress, happiness, effect, and welfare reflects the expectations for welfare services and promotion of public health using agrohealing for all citizens that include persons with disabilities and dementia as well as general citizens. In particular, it was possible to identify the trend in positive mental and emotional effects such as stress relief and happiness. This shows that the discourse on agro-healing is formed and expanded, showing public needs and interest in solving public health problems and seeking a healthy life.

Conclusion

The enforcement of the Act on Research, Development, and Promotion of Healing Agriculture in March 2021 has led to a growing interest in agro-healing and active R&D by the RDA to lay the groundwork for agro-healing (Yoo et al., 2021). Moreover, as the five-year comprehensive plan for healing agriculture begins to be established since 2022 according to Article 5 in Chapter 2 of the Act on Research, Development, and Promotion of Healing Agriculture, there have been more and more discourses on agro-healing. Accordingly, this study conducted semantic network analysis to provide the basic data to activate related policies and research by analyzing online news articles on agro-healing.

Based on the results, the keywords were clustered into four groups: ‘agro-healing characteristics’, ‘agro-healing resources’, ‘agro-healing activities’, and ‘agro-healing target and effect’. The results of identifying the trend in the social discourse on agro-healing can be summarized as follows. First, the discourse first began to be formed in 2012 when agro-healing was selected as a noteworthy keyword in agricultural and industrial changes. Issues have been raised in the need to provide services using agro-healing facilities or resources through rural area development.

Second, words such as healing, care farms, vitality, and effect were mentioned throughout all years, indicating that there is an ongoing trend that aims for sustainable growth of agriculture and rural areas based on expectations for the value and role of rural areas as a healing space as well as activation of agro-healing. This provides great implications for the factors to consider first in agro-healing policies as well as the future direction.

Third, the main discourse was on the interest and importance of providing welfare services at the national level to solve mental health problems such as stress and dementia through agro-healing and reducing social costs in prevention, recovery, and rehabilitation of mental and physical diseases. In particular, the explosive increase in the number of articles on agro-healing since 2020 may have been due to the fact that social expectations and need for agro-healing to promote public health became a significant part of the discourse due to the enforcement of the Act on Research, Development, and Promotion of Healing Agriculture and the COVID-19 pandemic that caused mental and physical health problems like depression and anxiety.

This study has significance in verifying the social expansion of the discourse on agro-healing by identifying the agro-healing trend through newspaper articles and analyzing big data generated in the last 10 years. However, this study is limited in that it analyzed only online news articles on the Naver News platform while excluding traditional news media such as TV and radio. By collecting unstructured data in various subjects of discourse, it will be possible to more closely analyze the social perception or trend. In addition, the results cannot be generalized since this study analyzed only the titles and main text of the articles. By also analyzing the comments and time-series data of the news articles, it would be possible to develop discussions from multiple angles based on the changes in public emotional reactions over time.

Notes

This work was supported by Gyeongsang National University Grant in 2022.

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Article information Continued

Fig. 1

Data analysis flowchart.

Fig. 2

Heatmap of articles mentioning agro-healing by month.

Fig. 3

Visualization with CONCOR analysis.

Table 1

Top 30 frequent keywords of agro-healing in online news articles

Rank Keywords Freq.z Rank Keywords Freq. Rank Keywords Freq.
1 Agro-healing 2,071 11 Dementia 1,252 21 Targets 1,250
2 Rural area 1,573 12 Health 1,223 22 Vitality 1,249
3 Program 1,489 13 Plan 1,219 23 Environment 1,249
4 Care farms 1,485 14 Effect 1,210 24 University 1,245
5 Education 1,456 15 Resources 1,206 25 Welfare 1,243
6 Support 1,355 16 Rural tourism 1,205 26 Plants 1,241
7 Healing 1,307 17 Vegetable garden 1,193 27 Citizen 1,187
8 Operation 1,276 18 RDA 1,179 28 Stress 1,183
9 Urban 1,262 19 Village 1,160 29 Future 1,152
10 Society 1,253 20 Development 1,130 30 Industry 1,148
z

Freq.: Frequency.

Table 2

Top 5 frequent keywords of agro-healing in online news articles by year

Year of

2012 2013 2014 2015 2016





Keywords Freq.z Keywords Freq. Keywords Freq. Keywords Freq. Keywords Freq.
Rural area 64 Support 60 Healing 188 Care farms 259 Healing 291
Healing 43 Vegetable garden 58 Industry 179 Rural area 151 Education 249
Society 42 Healing 45 Targets 148 Urban 122 Citizen 230
Health 39 Program 41 Plants 135 Operation 102 Rural urism to 220
Environment 35 Village 39 Education 75 Healing 84 Resources 216

Year of

2017 2018 2019 2020 2021





Keywords Freq. Keywords Freq. Keywords Freq. Keywords Freq. Keywords Freq.

Healing 326 Education 345 Development 421 Support 585 Agro-healing 562
Rural area 260 Care farms 341 Care farms 416 Agro-healing 506 Dementia 508
Vitality 247 Welfare 324 Agro-healing 402 Dementia 421 Stress 466
Program 199 Future 327 Effect 377 RDA 413 Society 421
Rural tourism 176 Healing 315 Welfare 298 COVID-19 372 COVID-19 403
z

Freq.: Frequency.

Table 3

Semantic network analysis of top 50 related words

Degree Centrality Betweenness Centrality Eigenvector Centrality



Keywords Coef.z Keywords Coef. Keywords Coef.
Agro-healing 1.00 Agro-healing 0.75 Agro-healing 0.98
Healing 0.95 Healing 0.69 Vitality 0.86
Care farms 0.94 Stress 0.67 Healing 0.81
Vitality 0.93 Urban 0.67 Care farms 0.81
RDA 0.90 Disabilities 0.67 Effect 0.78
Citizen 0.90 Care farms 0.67 Citizen 0.74
Rural tourism 0.90 Dementia 0.67 RDA 0.74
Family 0.88 Rural area 0.67 Family 0.74
Education 0.85 Welfare 0.60 Rural tourism 0.74
Rural area 0.85 Field 0.60 Education 0.60
Farmhouse 0.84 Patient 0.60 Farmhouse 0.59
Cure 0.84 Experience 0.58 Patient 0.58
Environment 0.83 Participation 0.58 Program 0.58
Program 0.83 RDA 0.58 Environment 0.53
Patient 0.83 Vitality 0.58 Operation 0.53
Industry 0.83 Health 0.54 Plants 0.53
Effect 0.80 Education 0.54 Stress 0.52
Disabilities 0.80 Happiness 0.54 Job creation 0.52
Urban 0.80 Rural tourism 0.52 Cure 0.31
Culture 0.80 Industry 0.31 Happiness 0.31
Dementia 0.79 Vegetable garden 0.29 Industry 0.31
Field 0.79 Nature 0.28 Urban 0.30
Plan 0.79 Service 0.27 Rural area 0.30
Participation 0.79 Effect 0.27 Disabilities 0.30
Resources 0.79 Program 0.26 Support 0.30
Vegetable garden 0.78 Environment 0.25 Dementia 0.30
Cultivation 0.67 Operation 0.25 Society 0.30
Welfare 0.67 Village 0.24 Future 0.30
Operation 0.67 Society 0.24 Health 0.30
Job creation 0.67 Future 0.23 Expert 0.21
Stress 0.67 Resources 0.23 Experience 0.21
Smart 0.67 Expert 0.23 Mind 0.21
University 0.64 Plan 0.22 Field 0.21
Youths 0.64 Mind 0.22 Plan 0.21
Horticulture 0.64 Cure 0.22 Participation 0.21
Development 0.63 Targets 0.21 Service 0.19
Urban agriculture 0.63 Culture 0.20 Resources 0.19
Support 0.63 Family 0.19 Welfare 0.19
Future 0.63 Support 0.18 Urban agriculture 0.18
Nature 0.63 Job creation 0.17 Village 0.18
Targets 0.63 Farmhouse 0.14 Horticulture 0.17
Health 0.63 Cultivation 0.09 Vegetable garden 0.17
Service 0.59 Development 0.09 Targets 0.15
Happiness 0.59 University 0.09 Nature 0.15
Experience 0.59 Plants 0.09 University 0.14
Plants 0.58 Citizen 0.09 Youths 0.14
Village 0.58 Horticulture 0.08 Smart 0.14
Society 0.58 Urban agriculture 0.08 Culture 0.14
Mind 0.58 Youths 0.08 Development 0.14
Expert 0.58 Smart 0.08 Cultivation 0.14
z

Coef.: Coefficient.