Text Network Analysis Research Articles

Research trends related to problematic smartphone use among school-age children including parental factors: a text network analysis.

This study aimed to identify the main keywords and research topics used in research on problematic smartphone use (PSU) among children (6-12 years old), including parental factors. The publication period for the literature was set from January 2007 to January 2022, as smartphones were first released in 2007. In total, 395 articles were identified, 230 of which were included in the final analysis. Text network analysis was performed using NetMiner 4.5. Research on this topic has steadily increased since 2007, with 40 papers published in 2021. Eight main research topics were derived: group 1, parental attitudes; group 2, children's PSU behavior and parental support; group 3, family environment and behavioral addiction; group 4, social relationships; group 5, seeking solutions; group 6, parent-child relationships; group 7, children's mental health and school adaptation; and group 8, PSU in adolescents. Parental factors related to PSU have been studied in various aspects. However, more active research on school-age children's PSU needs to be conducted due to the paucity of research in this population compared to studies conducted among adolescents. The results of this study provide useful data for selecting research topics in the field of PSU.

Isomorfisme Institusional LPSK dalam Penegakan Hak Rehabilitasi Psikososial Korban Tindak Pidana di Indonesia

This study explains how isomorphism develops in the Indonesian Witness and Victim Protection Agency (Lembaga Perlindungan Saksi dan Korban-LPSK) and contributes to stronger coordination among institutions in administering psychological services for crime victims in Indonesia. This study employs an organizational sociology approach with an isomorphism perspective to determine the robustness of institutions' roles and relationships in providing psychosocial care to crime victims. Using the Soft Systems Methodology (SSM) in conjunction with Textual Network Analysis (TNA), this study discovered that coercive isomorphism in government organizations is the primary driving factor behind mimetic and normative isomorphism, whereas stereotyped isomorphism drives the private sector. Isomorphism emerges to give psychosocial services to victims of criminal crimes, namely as a result of reinforcement from Law Number 31 of 2014 on the Protection of Witnesses and Victims. This reinforcement prompted mimetic isomorphism in many forms of cooperation, and as a result, normatively, it is feasible to claim that LPSK has evolved into a professional institution in delivering psychological rights services to victims of criminal crimes. Furthermore, this study makes recommendations on the role of state institutions and cooperative institutions in satisfying the rights of crime victims in Indonesia, particularly psychosocial assistance.

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria.

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.

Who Drives Carbon Neutrality in China? Text Mining and Network Analysis

China has recently declared its role as a leading developing country in actively practicing carbon neutrality. In fact, its carbon-neutral policy has accelerated from a gradual and macroscopic perspective and has been actively pursued given the changes not only in the overall social system but also in its impact on various stakeholders. This study analyzed the patterns of carbon neutrality (CN) and the actors of policy promotion in China from a long-term perspective. It collected policy discourses related to CN posted on Chinese websites from 2000 to 2022 and conducted text mining and network analysis. The results revealed that the pattern of CN promotion in China followed an exploration–demonstration–industrialization–digitalization model, similar to other policies. Moreover, the policy promotion sector developed in the direction of unification–diversification–specialization. Analysis of policy promotion actors found that enterprises are the key driver of continuous CN. In addition, the public emerged as a critical actor in promoting CN during the 12th–13th Five-Year Plans (2011–2020). Moreover, the central government emerged as a key driving actor of CN during the 14th Five-Year Plan. This was a result of the emphasis on efficiency in the timing and mission process of achieving CN. Furthermore, based on the experience of COVID-19, the rapid transition of Chinese society toward CN emphasizes the need for a central government with strong executive power. Based on these results, this study presents constructive suggestions for carbon-neutral development in China.

Research Trends of Lifestyle Intervention for Women With Obesity: A Text Network Analysis.

While the number of lifestyle intervention studies for women with obesity continues to increase, the research trends related to this topic should be evaluated through text network analysis. A total of 231 relevant studies published in international journals between 2011 and 2021 were identified. Semantic morphemes of the abstracts were refined, and a co-occurrence matrix with 117 keywords was generated using the text network analysis program NetMiner 4.3. The core keywords were determined as the top 25 in degree centrality, closeness centrality, or betweenness centrality. 'Lifestyle', 'Intervention', 'Diet', 'Exercise', 'Diabetes mellitus', 'Body', 'Composition', 'Quality of life', 'Obesity', 'Gain', 'Diet', and 'Loss' were the most frequently used keywords. The findings of this study provide a general overview of the research trends in lifestyle interventions for women with obesity and may be used as reference for future research.

Analysis of News Regarding on Shared Housing Using Text Mining Techniques

This study was conducted to explore the development direction of shared housing as a sustainable housing product by exploring the perception of shared housing and related words through Big data analysis and examining the correlation between keywords. Data was collected using Big Kinds, a news search service, and sentiment analysis, text mining analysis, N-gram and network analysis, and CONCOR analysis were performed using TEXTOM and UCINET, which are Big data analyses programs. As a result of the analysis. First, shared housing began to be introduced as a new housing type in late 2010 and began to be supplied in earnest in 2013. Second, the sensibility for shared housing has a liking and interest in the new space, but there is also a sense of rejection of living together. Third, as a result of keyword frequency analysis, the frequency of occurrence of related keywords indicating the residence of shared housing such as youth and single-person households and keywords indicating the characteristics of youth housing policy projects was high. Fourth, CONCOR analysis found significant four cluster characteristics: ‘Share house’, ‘Co-living’, ‘Remodeling social housing’, and ‘Young people rental housing’.

Artificial Intelligence Technology-Based Semantic Sentiment Analysis on Network Public Opinion Texts

Considering that the current social network text analysis works poorly in accurate and effective sentiment prediction and management, a deep learning (D-L)-based text sentiment analysis method is proposed for the big data environment. First, the autoregressive language model mode XLNet is used to capture bidirectional text information and a sentiment analysis model XLNet-Multi-Attention-BiGRU. Then, considering the context information of social network texts, the defect of traditional GRU units only reading texts in order is overcome by introducing a BiGRU model to extract features in both directions. Finally, a multi-headed attention layer is added between the BiGRU and CRF layers to better capture the key information in the sentence by integrating multiple single-head attention. The results show that the precision, recall, and F1 value of the method proposed in this paper are the largest, with the highest reaching 92.64%, 92.32%, and 91.25%, respectively, which are 12.40%, 10.17%, and 9.63% higher than the maximum values of the other three methods, respectively.

Listen to social media users: Mining Chinese public perception of automated vehicles after crashes

In China, vehicles with L2 driving automation are already widespread, while L3-L5 automated vehicles (AVs) are not allowed on the road. Surprisingly, crashes involving vehicles with L2 driving automation contributed to the public’s discussion of L3-L5 AVs. Understanding the variations in public perception of AVs is imperative, which could significantly affect the future evolution of AVs. Respondents have few opportunities to express their views freely in survey-based studies relying on structured questionnaires (e.g., Likert scale). We collected 42,111 comments from Chinese mainstream social media platforms (Sina Weibo and Tik Tok) in August 2021 and applied advanced text mining technology (Latent Dirichlet Allocation topic model, text network analysis, and sentiment analysis) to understand variations in public perception of AVs before and after the crashes. The results show that the public expresses more blame for vehicles with L2 driving automation than human drivers and has misunderstandings about the current development of L3-L5 AVs, which is closely connected with publicity from automobile companies and media. Women were more negative than men both before and after the crash, and the sentiment difference was most significant in the first three days of the crash. This research provides new ideas for a timely understanding of the public perception of AVs. Findings contribute to specific policy recommendations to help in the technological change and wider adoption of AVs.

청소년 국제체험학습 관련 국내 연구 동향 분석(2015-2022)

This study aims to analyze trends in domestic research on international experiential learning targeting youth since 2015, to find out tasks and future directions. For the research trend analysis, 127 papers were selected from KISS using keywords related to ‘area’, ‘type’, ‘scope’. The papers were analyzed utilizing Network Text Analysis. The results were as follows. First, the number of publications, while having shown a significant decrease since 2015, started to increase in 2017. Second, a great number of studies was published by international education-related academies. Third, while ‘overseas volunteer’ was the most prevalent research subject, ‘secondary education’ was the least popular one. Fourth, the cluster analysis result revealed a strong connection between ‘2019’ and ‘overseas volunteer', which the policy ‘Job creation through overseas volunteer’, undertaken since 2018, seemed to have an impact. Fifth, ‘online exchange' shows a low value both in frequency and centrality degree. The implications were as follow. First, since the research about ‘secondary education’ has small numbers, more research needs to be conducted. Second, regarding ‘overseas volunteer’ commonly used for personal interest like ‘building up employment qualification’, further discussions on the purpose and meaning of it are required. Third, more research on ICT-based and ICT blended-related are needed. Lastly, the number of researchers who has continuously studied on ‘international experiential learning’ for youth is not enough regarding that the frequency and keywords of studies have been easily affected by policies and social phenomena. Therefore, constant effort by researchers seems to be needed.

Tone at the Top for Sustainable Corporate Governance to Prevent Fraud

This paper aims to provide a systematic literature review of sustainable corporate governance to prevent fraud through the tone at the top perspective. In recent years, studies on corporate governance and sustainability have considerably increased. The main aspects of the intersection of these fields were analyzed, especially considering the role of fraud and risk management. Indeed, corporate fraud can hinder corporate sustainability goals. However, to remove fraudulent behavior, rules of conduct, formal recommendations, or the implementation of business ethics programs are often insufficient. In this sense, corporate ethics linked to governance has been referred to as “tone at the top”. Given the significant progress in this area and the lack of a generally accepted theory, an exhaustive organization of the research is needed. Based on these assumptions, this study employed text network analysis to systematically analyze the research contributions collected from the Scopus database for the period 1990–2021. This study establishes networks using the main information of scientific contributions, such as “Abstract”, “Title”, and “Keywords”, and performs analyses, such as co-occurrence and content analyses. The main findings highlight the growing importance of corporate governance in sustainability and propose the emerging role of tone at the top as one of the main drivers of corporate governance sustainability to prevent fraud. We conclude by suggesting some insights derived from the study. The results could be useful for both the academic and professional communities, offering an opportunity for future research.

Extending the Behavioral Geography within the Context of Forest Restoration: Research on the Geographical Behaviors of Northern-Migrating Asian Elephants (Elephas maximus) in Southwest China

In 2021, the northward migration of Asian elephants in southwestern China’s Yunnan Province attracted significant public attention. Exploring the behavior of Asian elephants will help to better protect this endangered species and further realize the harmonious coexistence of humans and elephants. Based on the news texts regarding the northward migration of Asian elephants, this study used network text analysis, social network analysis, and grounded theoretical research methods to explore the behavioral characteristics and internal motivations of Asian elephants during their northward migration. The results indicate that: (1) during the northward migration of Asian elephants, moving and foraging are their most frequent behaviors, and foraging may be the chief purpose of the migration. (2) Different behaviors of Asian elephants hide their behavioral choice preferences, including environmental, time, and behavioral preferences. During the migration, Asian elephants mostly move in low-altitude areas, often foraging or migrating around farmland in the afternoon or at night, returning to the mountains to rest in the early morning. Corn, rice, and other crops are their primary food; the change in their eating habits is influenced by the lack of herbs and woody plants inside the protected area. (3) The northward migration behavior of elephant herds is influenced by various factors, such as elephant population expansion, habitat change, and species migration characteristics, and the relationship between conservation and development needs to be balanced.

Text Network-Based Method for Measuring Hand Functions in Degenerative Brain Disease Patients

In this study, we collected various past study results on tools and analytical methods for measuring hand functions of patients with degenerative brain diseases, such as Parkinson’s disease and stroke, and selected and proposed appropriate hand function measurement tools, methods, and analysis software based on text network analysis. We searched the literatures using keywords related to degenerative brain disease and stroke patients for participant types, use of devices and sensors for the intervention types, and hand function assessment for measurement types. Among the 2484 literatures collected, 19 were eventually selected based on certain inclusion and exclusion criteria. As a result of text network analysis, the degree-centrality and the betweenness centrality were the highest in the keyword of Parkinson’s disease for the participant type, force sensor for the intervention type, and finger tapping for the measurement type. Based on these results, pinch gloves comprising an FSR sensor were manufactured, and software and contents were implemented to measure and analyze various quantitative parameter values during finger tapping. The software can evaluate endurance and agility by measuring the finger-tapping intensity and operation time using the index finger and thumb. The contents can evaluate the stability of hand functions by analyzing the coefficient of variation of the tapping interval, the average contact time, and the accuracy of hand functions by analyzing the reaction rate to the presented visual stimulus. As a result of comparing hand functions through 10 types of analysis parameters with a sample of 12 ordinary subjects (8 men and 4 women) using the manufactured pinch gloves, there was a difference between the two genders in the items evaluating muscle strength and agility, and a significant difference in the analysis parameters evaluating stability and accuracy. The results indicate that using the text network analysis-based hand function measurement tool and the method proposed in this study should help derive the objective research results as well as a quantitative comparison of research results of various researchers.

COVID-19 and Public Health: Analysis of Opinions in Social Media.

The article presents the results of research of public opinion during the third wave of the COVID-19 pandemic in Russia. The study touches on the attitude of citizens to public health, as well as the reaction of social media users to government measures in a crisis situation during a pandemic. Special attention is paid to the phenomenon of infodemic and methods of detecting cases of the spread of false and unverified information about diseases. The article demonstrates the application of an interdisciplinary approach using network analysis of texts and sociological research. A model for detecting social stress in the textual communication of social network users using a specially trained neural network and linguistic analysis methods is presented. The validity and validity of the results of the analysis of social network data were verified using a sociological survey. This approach allows us to identify points of tension in matters of public health promotion, during crisis situations to improve interaction between the government and society, and to timely adjust government plans and actions to ensure resilience in emergency situations for public health purposes.

National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling

This study aimed to identify the main keyword, network structure, and main topics of the national petition related to "nursing" in South Korea. Data were gathered from petitions related to the national petition in Korea Blue House related to the topic "nursing" or "nurse" from August 17, 2017, to May 9, 2022. A total of 5,154 petitions were searched, and 995 were selected for the final analysis. Text network analysis and topic modeling were analyzed using the Netminer 4.5.0 program. Regarding network characteristics, a density of 0.03, an average degree of 144.483, and an average distance of 1.943 were found. Compared to results of degree centrality and betweenness centrality, keywords such as "work environment," "nursing university," "license," and "education" appeared typically in the eigenvector centrality analysis. Topic modeling derived four topics: (1) "Improving the working environment and dealing with nursing professionals," (2) "requesting investigation and punishment related to medical accidents," (3) "requiring clear role regulation and legislation of medical and nonmedical professions," and (4) "demanding improvement of healthcare-related systems and services." This is the first study to analyze Korea's national petitions in the field of nursing. This study's results confirmed both the internal needs and external demands for nurses in South Korea. Policies and laws that reflect these results should be developed.

Emotion Exploration in Autistic Children as an Early Biomarker through R-CNN

Autism Spectrum Disorder (ASD) is found to be a major concern among various occupational therapists. The foremost challenge of this neurodevelopmental disorder lies in the fact of analyzing and exploring various symptoms of the children at their early stage of development. Such early identification could prop up the therapists and clinicians to provide proper assistive support to make the children lead an independent life. Facial expressions and emotions perceived by the children could contribute to such early intervention of autism. In this regard, the paper implements in identifying basic facial expression and exploring their emotions upon a time-variant factor. The emotions are analyzed by incorporating the facial expression identified through Convolution Neural Network (CNN) using 68 landmark points plotted on the frontal face with a prediction network formed by Recurring Neural Network (RNN) known as the RCNN based Facial Expression Recommendation (FER) system. The paper adopts Recurring Convolution Neural Network (R-CNN) to take the advantage of increased accuracy and performance with decreased time complexity in predicting emotion as textual network analysis. The papers prove better accuracy in identifying the emotion in autistic children when compared over simple machine learning models built for such identifications contributing to autistic society.

Research on Tourist Perception and Quality Improvement of Dali B&B: Based on Network Text Analysis

Studying the high-quality development of B&B based on the perspective of tourist experience is an important way to fit the development of high-quality tourism B&B and meet the market demand. Taking Dali Homestay gathering area as the research object, we collect the comments of tourists on Ctrip travel website, use Octoparse 8.0 web crawler software to obtain a large number of online comment text, and use ROST-CM6 software and content analysis method to explore the tourists' experience and perception of Dali B&B. The results show that the tourists mainly focus on high frequency words and around the perception of the semantic network are "room" and "host" and "clean" and "enthusiasm", that visitors to the B&B facility itself hardware facilities and software services, its feelings focus on the basic accommodation function of B&B facility, no special perception of local culture characteristics. Tourists' overall perception and experience of Dali B&B is good, and the negative emotions are mainly reflected in the imperfect infrastructure of B&B, inadequate service, excessive commercialization tendency and weak product experience of B&B. Therefore, the need to further improve the B&B facility infrastructure to improve the level of hardware, provide personalized service to ensure service quality, the introduction of relevant professionals to improve the management level, strengthen the supervision and management of B&B facility market to promote the healthy development, make tourists get better perception experience, guarantee the high quality of Dali B&B facility industry development.

Identifying the Knowledge Structure and Trends of Nursing Informatics: A Text Network Analysis.

With the advent of the information age and technological development, the importance of digital health technologies has increased. Subsequently, nursing informatics has been developed to enhance the effectiveness of healthcare information management and communication. This study aimed to identify the nursing informatics knowledge structure and research trends through quantitative analysis using text network analysis. Here, we analyzed 14 225 studies published by 2020. The knowledge structure of nursing informatics and changes therein were clarified by identifying and analyzing the core keywords, topics, and changes in the topics of related studies over time. We identified "patient," "health," "system," and "information" as core keywords connecting other keywords. Over time, the networks between "information," "communication," and "technology" strengthened, and "patient safety" and "quality" have recently emerged as research keywords. This change indicates an increase in the importance of nursing education on technology. Similar changes appeared in the topic analysis, showing an increased proportion of research related to system and technology and nursing education. These results can broaden a systematic understanding of nursing informatics research. Furthermore, given these findings, the importance of nursing informatics on patient safety and nursing education-based on the development of systems and technology-can be expected to continue growing.

The Early Emotional Responses and Central Issues of People in the Epicenter of the COVID-19 Pandemic: An Analysis from Twitter Text Mining

This study aimed to explore citizens’ emotional responses and issues of interest in the context of the coronavirus disease 2019 (COVID-19) pandemic. The dataset comprised 65,313 tweets with the location marked as New York State. The data collection period was four days of tweets when New York City imposed a lockdown order due to an increase in confirmed cases. Data analysis was performed using R Studio. The emotional responses in tweets were analyzed using the Bing and NRC (National Research Council Canada) dictionaries. The tweets’ central issue was identified by Text Network Analysis. When tweets were classified as either positive or negative, the negative sentiment was higher. Using the NRC dictionary, eight emotional classifications were devised: “trust,” “fear,” “anticipation,” “sadness,” “anger,” “joy,” “surprise,” and “disgust.” These results indicated that citizens showed negative and trusting emotional reactions in the early days of the pandemic. Moreover, citizens showed a strong interest in overcoming and coping with other people such as social solidarity. Citizens were concerned about the confirmation of COVID-19 infection status and death. Efforts should be made to ensure citizens’ psychological stability by promptly informing them of the status of infectious disease management and the route of infection.

Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing

The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

Who Tells Your Story? Analyzing a Century of Utah History

Who Tells Your Story? Analyzing a Century of Utah History