Using Text Network Analysis for Analyzing Academic Papers in Nursing

Purpose: This study examined the suitability of using text network analysis (TNA) methodology for topic analysis of academic papers related to nursing. Methods: TNA background theories, software programs, and research processes have been described in this paper. Additionally, the research methodology that applied TNA to the topic analysis of the academic nursing papers was analyzed. Results: As background theories for the study, we explained information theory, word co-occurrence analysis, graph theory, network theory, and social network analysis. The TNA procedure was described as follows: 1) collection of academic articles, 2) text extraction, 3) preprocessing, 4) generation of word co-occurrence matrices, 5) social network analysis, and 6) interpretation and discussion. Conclusion: TNA using author-keywords has several advantages. It can utilize recognized terms such as MeSH headings or terms chosen by professionals, and it saves time and effort. Additionally, the study emphasizes the necessity of developing a sophisticated research design that explores nursing research trends in a multidimensional method by applying TNA methodology

Determining the Topic Evolution and Sentiment Polarity for Albinism in a Chinese Online Health Community: Machine Learning and Social Network Analysis

There are more than 6000 rare diseases in existence today, with the number of patients with these conditions rapidly increasing. Most research to date has focused on the diagnosis, treatment, and development of orphan drugs, while few studies have examined the topics and emotions expressed by patients living with rare diseases on social media platforms, especially in online health communities (OHCs). Objective This study aimed to determine the topic categorizations and sentiment polarity for albinism in a Chinese OHC, Baidu Tieba, using multiple methods. The OHC was deeply mined using topic mining, social network analysis, and sentiment polarity analysis. Through these methods, we determined the current situation of community construction, identifying the ongoing needs and problems experienced by people with albinism in their daily lives. Methods We used the albinism community on the Baidu Tieba platform as the data source in this study. Term frequency–inverse document frequency, latent dirichlet allocation models, and naive Bayes were employed to mine the various topic categories. Social network analysis, which was completed using the Gephi tool, was employed to analyze the evolution of the albinism community. Sentiment polarity analysis was performed using a long short-term memory algorithm. Results We identified 8 main topics discussed in the community: daily sharing, family, interpersonal communication, social life and security, medical care, occupation and education, beauty, and self-care. Among these topics, daily sharing represented the largest proportion of the discussions. From 2012 to 2019, the average degree and clustering coefficient of the albinism community continued to decline, while the network center transferred from core communities to core users. A total of 68.43% of the corpus was emotional, with 35.88% being positive and 32.55% negative. There were statistically significant differences in the distribution of sentiment polarity between topics (P Conclusions The study reveals insights into the emotions expressed by people with albinism in the Chinese OHC, Baidu Tieba, providing health care practitioners with greater appreciation of the current emotional support needed by patients and the patient experience. Current OHCs do not exert enough influence due to limited effective organization and development. Health care sectors should take greater advantage of OHCs to support vulnerable patients with rare diseases to meet their evidence-based needs. </jats:sec

When Silver Is As Good As Gold: Using Weak Supervision to Train Machine Learning Models on Social Media Data

Over the last decade, advances in machine learning have led to an exponential growth in artificial intelligence i.e., machine learning models capable of learning from vast amounts of data to perform several tasks such as text classification, regression, machine translation, speech recognition, and many others. While massive volumes of data are available, due to the manual curation process involved in the generation of training datasets, only a percentage of the data is used to train machine learning models. The process of labeling data with a ground-truth value is extremely tedious, expensive, and is the major bottleneck of supervised learning. To curtail this, the theory of noisy learning can be employed where data labeled through heuristics, knowledge bases and weak classifiers can be utilized for training, instead of data obtained through manual annotation. The assumption here is that a large volume of training data, which contains noise and acquired through an automated process, can compensate for the lack of manual labels. In this study, we utilize heuristic based approaches to create noisy silver standard datasets. We extensively tested the theory of noisy learning on four different applications by training several machine learning models using the silver standard dataset with several sample sizes and class imbalances and tested the performance using a gold standard dataset. Our evaluations on the four applications indicate the success of silver standard datasets in identifying a gold standard dataset. We conclude the study with evidence that noisy social media data can be utilized for weak supervisio

A importância do ambiente urbano para o bem-estar: análise em Lisboa utilizando redes sociais

O objetivo da generalidade das pessoas é ser feliz. Alcançar esse feito depende de muitos fatores, alguns relativos ao local onde vivemos. Os especialistas referem que o segredo para a “cidade feliz” está ligado às pessoas, mais precisamente ao seu bem-estar e qualidade de vida. Este estudo tem o intuito de compreender o impacte que o ambiente urbano da cidade de Lisboa tem no bem-estar dos indivíduos. A análise do bem-estar com recurso à rede social Twitter permite identificar os locais em que o mal-estar e bem-estar prevalecem. Para a sua identificação são considerados um conjunto de variáveis que caracterizam a morfologia da cidade de Lisboa: popularidade dos locais, índices de forma urbana, zonas de sol/sombra, largura da rua, estrutura verde e azul, idade média do edificado, etc. Contudo, não é uma tarefa fácil porque as pessoas recebem diferentes tipos de informação sensorial de um espaço, tornando-se desafiador determinar quais os aspetos da experiência no ambiente urbano que afetam o bem-estar. É nesse sentido que se aplica a machine learning (ML), de modo a determinar quais as variáveis que influenciam o sentimento positiva e negativamente. O ML é reconhecido por alcançar resultados de exatidão superiores aos métodos tradicionais. Estes tipos de modelos apresentam diversas vantagens, tais como, a capacidade de lidar com dados de diferentes tipos, estruturas e quantidades (i.e., big data).The goal of most people is to be happy. Achieving this feat depends on many factors, some related to where we live. Experts say that the secret to a "happy city" is linked to people, more precisely to their well-being and quality of life. This study aims to understand the impact that the urban environment of the city of Lisbon has on the well-being of individuals. The analysis of well-being using the social network Twitter allows identifying the places where malaise and well-being prevail. For its identification are considered a set of variables that characterize the morphology of the city of Lisbon: the popularity of the places, indexes of urban shape, sun/shadow areas, the width of the street, green and blue structure, the average age of the building, etc. However, it is not an easy task because people receive different types of sensory information from space, making it challenging to determine which aspects of the experience in the urban environment affect well-being. In this context, machine learning (ML) is applied to determine which variables influence positive and negative sentiment. ML is recognized for superior accuracy results to traditional methods. These types of models have advantages, such as the ability to handle data of different types, structures, and quantities (i.e., big data)