Soil legislation and policies: Bibliometric analysis, systematic review and quantitative approaches with an emphasis on the specific cases of the European Union and Portugal.

The literature shows that there are dimensions related to soil legislation and policy in the European Union contexts that can be better explored through bibliometric analysis, systematic reviews and quantitative approaches. Therefore, this research aims to analyse documents on soil legislation and policies, highlighting the specific cases of Portugal and the European Union (EU). The aim is to identify suggestions to improve the Portuguese and European Union soil policy instruments and measures. To achieve these objectives, a bibliometric analysis (considering text and bibliographic data) and systematic review were carried out, as well as a survey of the available soil legislation (considering qualitative data and quantitative analysis). The results show that soil legislation and policy have become more relevant in recent years and that concerns are about soil health, protection and safety, as well as risk mitigation, biodiversity preservation and the maintenance of ecosystem services. However, some topics could be further explored in future research, namely those related to multidisciplinarity, smart methodologies, soil salinisation, innovation and quantitative approaches to assessing policy impacts. This study presents suggestions that can be considered by the Portuguese and European Union policymakers to improve the respective soil legislation and policies. Defining a regulatory system for soils in the European Union has not been easy over time, although there have been attempts, given the specificities of the contexts related to soils and the reluctance of some member states to take certain measures. The approaches and analysis topics considered are innovative (there aren't many scientific documents on the topics that address bibliometric analysis and quantitative assessments with qualitative data) and bring novelty to the literature.

Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000-2023).

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 framework was followed to identify, filter, and select relevant papers. This study used the Web of Science database to determine relevant publications concerning diabetes detection and classification using the keywords "diabetes detection", "diabetes classification", and "diabetes detection and classification". A total of 863 publications were selected for analysis. The research applied two bibliometric techniques: performance analysis and science mapping. Various bibliometric parameters, including publication analysis, trend analysis, citation analysis, and networking analysis, were used to assess the performance of these articles. The analysis findings showed that India, China, and the United States are the top three countries with the highest number of publications and citations on diabetes detection and classification. The most frequently used keywords are machine learning, diabetic retinopathy, and deep learning. Additionally, the study identified "classification", "diagnosis", and "validation" as the prevailing topics for diabetes identification. This research contributes valuable insights into the academic landscape of diabetes detection and classification.

Evolution of renewable energy laws and policies in China.

This study employs Latent Dirichlet Allocation (LDA) topic modelling methodology to analyze documents related to renewable energy laws and policies at the central level in China. The objective is to investigate the development and evolution of renewable energy policies in China and to gain insights into the national-level attitudes towards renewable energy development. The study consists of two phases: initially, renewable energy policy documents undergo keyword analysis using word clouds and keyword co-occurrence network analysis to elucidate the focal areas and their interconnections within the legal and policy texts. Subsequently, after determining the optimal number of topics for modelling based on topic perplexity and consistency results, the text undergoes data cleaning to isolate words with practical significance. These words are then incorporated into the LDA topic model to analyze the distribution and content of potential topics within the policies. Lastly, by linearly segmenting the time frame, changes in topic intensity over time are visually examined using heat maps. The findings indicate that energy policies have consistently prioritized "development" and emphasized the significance of "new energy" in renewable energy policies. Moreover, as renewable energy has progressed, governments and policymakers have come to acknowledge the importance of comprehensive energy planning, transitioning to clean energy sources, and regulating the electricity market. This growing awareness has led to efforts to strengthen policy and regulatory measures to foster renewable energy's sustainable development and utilization. In summary, this study highlights the effectiveness of the LDA topic model in analyzing renewable energy policies, advancing its adoption and furthering research in the field.

The bibliometric analysis of previous twenty- five years' literature: A microfinance review.

The tremendous increase in publications in Microfinance since 2000 has highlighted need for and importance of innovative techniques to present big data in this field in a most informative, scientific, and summarized manner. The study highlights the trends and patterns of Microfinance literature by revealing what has been done and what could be done in future. The study comprises of 1429 microfinance publications extracted from the Scopus database. The authors adopt bibliometric analysis through open software application R and network analysis techniques using Gephi AND VOS viewer software. The study adds a valuable contribution to the field of Microfinance by distinctively summarizing the important literature. It identifies global academic research trends and provides insights about trending topics, highly cited literature, authors, countries, collaboration network, word cloud, citation analysis, etc. Finally based on extensive literature survey through bibliometric analysis. The study highlights about the scope of future research in Microfinance.

What can we learn from a Chinese social media used by glaucoma patients?

PURPOSE: Our study aims to discuss glaucoma patients' needs and Internet habits using big data analysis and Natural Language Processing (NLP) based on deep learning (DL). METHODS: In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glaucoma bar of Baidu Tieba, China. According to the contents of topic posts, we classified them into posts with seeking medical advice and without seeking medical advice (social support, expressing emotions, sharing knowledge, and others). Word Cloud and frequency statistics were used to analyze the contents and visualize the keywords of topic posts. Two DL models, Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT), were trained to identify the posts seeking medical advice. The evaluation matrices included: accuracy, F1 value, and the area under the ROC curve (AUC). RESULTS: A total of 10,892 topic posts were included, among them, most were seeking medical advice (N = 7071, 64.91%), and seeking advice regarding symptoms or examination (N = 4913, 45.11%) dominated the majority. The following were searching for social support (N = 2362, 21.69%), expressing emotions (N = 497, 4.56%), and sharing knowledge (N = 527, 4.84%) in sequence. The word cloud analysis results showed that ocular pressure, visual field, examination, and operation were the most frequent words. The accuracy, F1 score, and AUC were 0.891, 0.891, and 0.931 for the BERT model, 0.82, 0.821, and 0.890 for the Bi-LSTM model. CONCLUSION: Social media can help enhance the patient-doctor relationship by providing patients' concerns and cognition about glaucoma in China. NLP can be a powerful tool to reflect patients' focus on diseases. DL models performed well in classifying Chinese medical-related texts, which could play an important role in public health monitoring.

Technology enabled communication during COVID 19: analysis of tweets from top ten Indian IT companies using NVIVO.

The corona virus (COVID-19) pandemic has impacted industries across the globe. Lockdown was imposed to curb the spread of the deadly virus. This resulted in closure of the factories and manufacturing units. Few sectors switched to work from home (WFH) for the first time. The present study aims to understand and analyze the way in which Information Technology (IT) sector communicated on Twitter during the pandemic. The top ten IT companies in India were selected on the basis of net sales. Qualitative data analysis was employed to extract tweets, understand and analyze them. Tweets were extracted from the official Twitter handles of these top ten IT companies using N-Capture extension tool of NVivo 12 software from April 1, 2020 to April 30, 2021. To get insights out of collected data, Word Cloud, TreeMap and Sentiment Analysis of tweets were carried out using NVivo 12 software. The research found that IT companies focussed on digital transformation, business development, customer satisfaction and enriching customer experience, new product development for healthcare and insurance and organizational resilience. They also focussed on effective communication through Twitter in times of crisis. Most of the companies tweeted moderately positive. Very small numbers of tweets were found to be very negative.

Lived experience-centred word clouds may improve research uncertainty gathering in priority setting partnerships.

INTRODUCTION: AO Spine RECODE-DCM was a multi-stakeholder priority setting partnership (PSP) to define the top ten research priorities for degenerative cervical myelopathy (DCM). Priorities were generated and iteratively refined using a series of surveys administered to surgeons, other healthcare professionals (oHCP) and people with DCM (PwDCM). The aim of this work was to utilise word clouds to enable the perspectives of people with the condition to be heard earlier in the PSP process than is traditionally the case. The objective was to evaluate the added value of word clouds in the process of defining research uncertainties in National Institute for Health Research (NIHR) James Lind Alliance (JLA) Priority Setting Partnerships. METHODS: Patient-generated word clouds were created for the four survey subsections of the AO Spine RECODE-DCM PSP: diagnosis, treatment, long-term management and other issues. These were then evaluated as a nested methodological study. Word-clouds were created and iteratively refined by an online support group of people with DCM, before being curated by the RECODE-DCM management committee and expert healthcare professional representatives. The final word clouds were embedded within the surveys administered at random to 50% of participants. DCM research uncertainties suggested by participants were compared pre- and post-word cloud presentation. RESULTS: A total of 215 (50.9%) participants were randomised to the word cloud stream, including 118 (55%) spinal surgeons, 52 (24%) PwDCM and 45 (21%) oHCP. Participants submitted 434 additional uncertainties after word cloud review: word count was lower and more uniform across each survey subsections compared to pre-word cloud uncertainties. Twenty-three (32%) of the final 74 PSP summary questions did not have a post-word cloud contribution and no summary question was formed exclusively on post-word cloud uncertainties. There were differences in mapping of pre- and post-word cloud uncertainties to summary questions, with greater mapping of post-word cloud uncertainties to the number 1 research question priority: raising awareness. Five of the final summary questions were more likely to map to the research uncertainties suggested by participants after having reviewed the word clouds. CONCLUSIONS: Word clouds may increase the perspective of underrepresented stakeholders in the research question gathering stage of priority setting partnerships. This may help steer the process towards research questions that are of highest priority for people with the condition.

Perfil de consumidores e sua percepção sobre antepasto de grão de bico contendo bactéria probiótica / Consumer profile and their perception of chickpea antipasto containing probiotic bacteria

Introdução: A indústria de alimentos e os pesquisadores têm-se dedicado a desenvolver novos produtos funcionais, com características mais naturais. Assim, estudos que identifiquem a demanda dos consumidores buscando atender seus anseios são importantes. Objetivo: Avaliar o perfil e a percepção de consumidores sobre antepastos, probióticos e a intenção de compras de um antepasto de grão de bico adicionado de bactéria probiótica. Método: A avaliação foi realizada de forma on-line, por meio de questionário contendo 33 questões respondidas por 322 participantes. Nuvens de palavras foram elaboradas com os resultados obtidos. Resultados: A maioria dos participantes reside na Região Sudeste, 72,7% são do gênero feminino, 37,3% possuem renda familiar de até três salários mínimos, 75,8% sabem o que é antepasto e mais da metade já consumiu grão de bico e conhece seus benefícios. Mais de 84,0% dos participantes sabem o que são probióticos e 90,1% já consumiram produtos probióticos de base láctea. Entretanto, 78,0% demonstraram interesse por opções de produtos probióticos de origem vegetal. Sobre as características que os participantes consideram que melhor descrevem o antepasto, as mais citadas foram: pastoso, macio, agridoce, salgado e firme. A nuvem de palavras mostrou que os respondentes associam probióticos à saúde intestinal e 36% deles estariam dispostos a comprar antepasto de grão de bico contendo probiótico se o produto estivesse disponível no mercado. Conclusão: O estudo indica que os consumidores têm interesse por grão de bico e probióticos, havendo uma demanda potencial por alimentos de origem vegetal contendo probióticos.

Introduction: The food industry and researchers have been dedicated to developing new functional products with more natural characteristics. Thus, studies that identify the demand of consumers seeking to meet their desires are important. Objective: To evaluate the profile and perception of consumers about antipasti, probiotics and purchase intention of a chickpea antipasti added with probiotic bacteria. Method: The evaluation was carried out online, through a questionnaire sent to 322 participants, containing 33 questions. Word clouds were created with the results obtained. Results: Most participants live in the Southeast region, 72.7% are female, 37.3% have a family income of up to three minimum wages, 75.8% know what antipasto is and more than half have consumed beak and knows its benefits. More than 84.0% of the participants know what probiotics are and 90.1% have already consumed dairy-based probiotic products. However, 78.0% showed interest in options for probiotic products of plant origin. About the characteristics that the participants consider that best describe the antipasto, the most cited were: Pasty, Soft, Bittersweet, Salty and Firm. The word cloud showed that respondents associate probiotics with gut health and 36% of those would be willing to buy probiotic-containing chickpea antipasto if the product were available on the market. Conclusion: The study indicates that consumers are interested in chickpeas and probiotics, with a potential demand for plant-based foods containing probiotics.

Detecting latent topics and trends in blended learning using LDA topic modeling.

With the rapid application of blended learning around the world, a large amount of literature has been accumulated. The analysis of the main research topics and development trends based on a large amount of literature is of great significance. To address this issue, this paper collected abstracts from 3772 eligible papers published between 2003 and 2021 from the Web of Science core collection. Through LDA topic modeling, abstract text content was analyzed, then 7 well-defined research topics were obtained. According to the topic development trends analysis results, the emphasis of topic research shifted from the initial courses about health, medicine, nursing, chemistry and mathematics to learning key elements such as learning outcomes, teacher factors, and presences. Among 7 research topics, the popularity of presences increased significantly, while formative assessment was a rare topic requiring careful intervention. The other five topics had no significant increase or decrease trends, but still accounted for a considerable proportion. Through word cloud analysis technology, the keyword characteristics of each stage and research focus changes of research were obtained. This study provides useful insights and implications for blended learning related research.

Hydrometeorological disasters during COVID-19: Insights from topic modeling of global aid reports.

Since the beginning of the COVID-19 pandemic, the world has experienced numerous hydrometeorological disasters along with it. The pandemic has made disaster relief work more challenging for humanitarian organizations and governments. This study aims to provide an overview of the topics/issues of concern in the countries while responding to hydrometeorological extreme events (e.g., floods and cyclones) during the pandemic. Latent Dirichlet Allocation (LDA), a computational topic modeling technique, is employed to reduce the numerous (i.e., 1771) humanitarian reports/news to key terms and meaningful topics for 24 countries. Several insights are derived from the LDA results. It is identified that countries have suffered multiple crises (such as locust attacks, epidemics and conflicts) during the pandemic. Maintaining social distancing while disaster evacuation and circumventing the lockdown for relief work have been difficult. Children are an important topic for most countries; however, other vulnerable groups such as women and the disabled also need to be focused upon. Hygiene is not a highly weighted topic, which is of concern during a pandemic that mandates good sanitation to control it effectively. However, health is of great importance for almost all countries. The novelty of the paper lies in its interdisciplinary approach (usage of a computational technique in disaster management studies) and the timely examination of disaster management experiences during the ongoing pandemic. The insights presented in the study may be helpful for researchers and policy-makers to initiate further bottom-up work to address the challenges in responding to hydrometeorological disasters during a pandemic.

Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques.

The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were "family", "anxiety", "house", and "life". Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

Trump's COVID-19 tweets and Dr. Fauci's emails.

The paper features an analysis of former President Trump's early tweets on COVID-19 in the context of Dr. Fauci's recently revealed email trove. The tweets are analysed using various data mining techniques, including sentiment analysis. These techniques facilitate exploration of content and sentiments within the texts, and their potential implications for the national and international reaction to COVID-19. The data set or corpus includes 159 tweets on COVID-19 that are sourced from the Trump Twitter Archive, running from 24 January 2020 to 2 April 2020. In addition we use Zipf and Mandelbrot's power law to calibrate the extent to which they differ from normal language patterns. A context for the emails is provided by the recently revealed email trove of Dr. Fauci, obtained by Buzzfeed on 1 June 2021 obtained under the Freedom of Information Act.

Salmonella spp. in Aquaculture: An Exploratory Analysis (Integrative Review) of Microbiological Diagnoses between 2000 and 2020.

The present study aimed to characterize, through descriptive statistics, data from scientific articles selected in a systematic integrative review that performed a microbiological diagnosis of Salmonella spp. in aquaculture. Data were obtained from research articles published in the BVS, Scielo, Science Direct, Scopus and Web of Science databases. The selected studies were published between 2000 and 2020 on samples of aquaculture animal production (fish, shrimp, bivalve mollusks, and other crustaceans) and environmental samples of aquaculture activity (farming water, soil, and sediments). After applying the exclusion criteria, 80 articles were selected. Data such as country of origin, categories of fish investigated, methods of microbiological diagnosis of Salmonella spp., sample units analyzed and most reported serovars were mined. A textual analysis of the word cloud and by similarity and descending hierarchical classification with the application of Reinert's algorithm was performed using R® and Iramuteq® software. The results showed that a higher percentage of the selected articles came from Asian countries (38.75%). Fish was the most sampled category, and the units of analysis of the culture water, muscle and intestine were more positive. The culture isolation method is the most widespread, supported by more accurate techniques such as PCR. The most prevalent Salmonella serovars reported were S. Typhimurium, S. Weltevreden and S. Newport. The textual analysis showed a strong association of the terms "Salmonella", "fish" and "water", and the highest hierarchical class grouped 25.4% of the associated text segments, such as "aquaculture", "food" and "public health". The information produced characterizes the occurrence of Salmonella spp. in the aquaculture sector, providing an overview of recent years. Future research focusing on strategies for the control and prevention of Salmonella spp. in fish production are necessary and should be encouraged.

Examining data visualization pitfalls in scientific publications.

Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran's Q test and McNemar's test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation.

The development of lived experience-centered word clouds to support research uncertainty gathering in degenerative cervical myelopathy: results from an engagement process and protocol for their evaluation, via a nested randomized controlled trial.

OBJECTIVES: AO Spine REsearch objectives and Common Data Elements for Degenerative Cervical Myelopathy [RECODE-DCM] is a multi-stakeholder consensus process aiming to promote research efficiency in DCM. It aims to establish the top 10 research uncertainties, through a James Lind Alliance Priority Setting Partnership [PSP]. Through a consensus process, research questions are generated and ranked. The inclusion of people with cervical myelopathy [PwCM] is central to the process. We hypothesized that presenting PwCM experience through word cloud generation would stimulate other key stakeholders to generate research questions better aligned with PwCM needs. This protocol outlines our plans to evaluate this as a nested methodological study within our PSP. METHODS: An online poll asked PwCM to submit and vote on words associated with aspects of DCM. After review, a refined word list was re-polled for voting and word submission. Word clouds were generated and an implementation plan for AO Spine RECODE-DCM PSP surveys was subsequently developed. RESULTS: Seventy-nine terms were submitted after the first poll. Eighty-seven refined words were then re-polled (which added a further 39 words). Four word clouds were generated under the categories of diagnosis, management, long-term effects, and other. A 1:1 block randomization protocol to assess word cloud impact on the number and relevance of PSP research questions was generated. CONCLUSIONS: We have shown it is feasible to work with PwCM to generate a tool for the AO Spine RECODE-DCM nested methodological study. Once the survey stage is completed, we will be able to evaluate the impact of the word clouds. Further research will be needed to assess the value of any impact in terms of stimulating a more creative research agenda.

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models.

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88-87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

Acute stroke awareness of family physicians: translation of policy to practice.

BACKGROUND: Translating clinical guidelines into routine clinical practice is mandatory to achieve population level improvement of health. Emergence of specific therapy for acute stroke yielded the 'time is brain' concept introducing the need for emergency treatment, pointing to the need for increasing stroke awareness of the general population. General practitioners (GPs) manage chronic diseases and could hence catalyse stroke awareness. In our study, the knowledge of general practitioners toward accurate identification of acute stroke candidacy was investigated. METHODS: GPs and residents in training for family medicine participated in a survey on a voluntary basis using supervised self-administration between the 1st of February 2018 and 31st July 2018. Two clinical cases of acute stroke that differed only regarding the patient's eligibility for intravenous thrombolysis were presented. Participants answered two open-ended questions. Text analysis was performed using NVIVO software. RESULTS: Of the 127 respondents, 69 (54.3%) were female. The median age was 49 (34-62) years. The median time spent working after graduation was 14.5 (2-22.5) years. Board-certified GPs made up 77.2% of the sample. Qualitative analysis revealed stroke as the most frequent diagnosis for both cases. Territorial localization and possible aetiology were also established. Respondents properly identified eligibility for thrombolysis. Quantitative assessment showed that having the practice closer to the stroke centre increases the likelihood of adequate diagnosis for acute stroke. CONCLUSIONS: Our results show that GPs properly diagnose acute stroke and identify intravenous thrombolysis candidates. Moreover, we found that a vigorous acute stroke triage system facilitates the translation of theory into practice.

Influence of the pandemic dissemination of COVID-19 on facial rejuvenation: A survey of Twitter.

BACKGROUND: With the pandemic dissemination of COVID-19, attitude and sentiment surrounding facial rejuvenation have evolved rapidly. AIMS: The purpose of this study was to understanding the impact of pandemic on the attitude of people toward facial skin rejuvenation. METHODS: Twitter data related to facial rejuvenation were collected from January 1, 2020, to April 30, 2020. Sentiment analysis, frequency analysis, and word cloud were performed to analyze the data. Statistical analysis included two-tailed t tests and chi-square tests. RESULTS: In the post-declaration, the number of tweets about facial rejuvenation increased significantly, and the search volume in Google Trends decreased. Negative public emotions increased, but positive emotions still dominate. The words frequency of "discounts" and "purchase" decreased. The dominant words in word cloud were "Botox," "facelift," "hyaluronic," and "skin." CONCLUSION: The public has a positive attitude toward facial rejuvenation during the pandemic. In particular, minimally invasive procedures dominate the mainstream, such as "Botox," "Hyaluronic acid," and "PRP." The practitioners could understand the change of the public interest in facial rejuvenation in time and decide what to focus on.

Análisis de la percepción del estudiante universitario sobre el contexto sociotecnológico a través de la innovación didáctica / Analysis of the university student's perception of the sociotechnological context through didactic innovation / Análise da percepção do estudante universitário sobre o contexto sociotecnológico através da inovação didática

Resumen Introducción. En el presente artículo se presenta un estudio diacrónico en el que se analizan 405 concepciones de estudiantes universitarios sobre la Sociedad de la Información implementadas a través de un software social (Word Clouds). Objetivo. El objetivo principal consiste en analizar la funcionalidad didáctica de herramientas de creación y difusión de contenido digital para el desarrollo de contenidos de asignaturas universitarias. Materiales y métodos. La muestra participante está compuesta por estudiantes de dos titulaciones: Doble Grado en Trabajo Social y Educación Social correspondientes a los cursos académicos 2010/11 a 2016/17 y Grado de Educación Social (2013/2017) de la Universidad Pablo de Olavide de Sevilla (España). A través de una metodología descriptiva y cualitativa se realiza la codificación y análisis de frecuencias temáticas de las nubes de palabras creadas por los estudiantes correspondiente al curso académico 2011-12 del doble Grado de Trabajo Social y Educación Social (T.S/E.S) y se comparan con los restantes cursos académicos. Resultados. Los resultados muestran que los términos más representativos para los estudiantes son: Globalización (61,86%), Comunicación (43,85%), Tecnología (43,85%), Tecnologías de la Información y Comunicación (TIC) (41,73%), Información (38,98%), e Internet- Red de redes (25,21%). Por último, cabe resaltar que dieciséis nuevos términos aparecen, dependiendo del año académico, siendo los más mencionados: Desigualdad (19,91%), Crisis (13,13%) y Consumo (8,47%). Conclusión. Esta experiencia universitaria permite mostrar que la utilización educativa de creación de nubes de palabras digitales puede ser un recurso didáctico muy interesante como vehículo para el sedimento reflexivo y repositorio de experiencias de aprendizaje del alumnado universitario.

Abstract Introduction. In the present article a diachronic study is presented in which 405 conceptions of university students about the Information Society are analyzed through a social software resource (Word Clouds). Objective. The main objective is to analyze the didactic functionality of tools for creating and disseminating digital content for the development of content for university subjects. Materials and methods. The participating sample is composed of students of two degrees: Double Degree in Social Work and Social Education corresponding to the academic courses 2010/11 to 2016/17 and Degree in Social Education (2013/2017) of the University of Pablo de Olavide / Seville (Spain). Through a descriptive and qualitative methodology, we code and analyse the thematic frequencies of the word clouds created by the students corresponding to the academic year 2011-12 of the double Degree of Social Work and Social Education (TS / ES). Thus, we compare them with the remaining academic courses. Results. The results show that the most representative terms for students are: Globalization (61.86%), Communication (43.85%), Technology (43.85%), Information and Communication Technologies (ICT) (41.73 %), Information (38.98%), and Internet- Network of networks (25.21%). Finally, it should be noted that sixteen new terms appear, depending on the academic year, being the most mentioned: Inequality (19.91%), Crisis (13.13%) and Consumption (8.47%). Conclusion. This university experience shows that the educational use of creating digital content with word clouds resources can be a very interesting didactic resource as a vehicle for reflection and a repository of learning experiences for university students.

Resumo Introdução. No presente artigo, é apresentado um estudo diacrônico em que 405 concepções de estudantes universitários sobre a Sociedade da Informação são analisadas por meio de um recurso de software social (Word Clouds). Objetivo. O objetivo principal é analizar a funcionalidade didática de ferramentas para criação e disseminação de conteúdo digital para o desenvolvimento de conteúdo para disciplinas universitárias. Materiais e métodos. A amostra participante é composta por estudantes de dois graus: Licenciatura Dupla em Serviço Social e Educação Social correspondente aos cursos acadêmicos 2010/11 a 2016/17 e Licenciatura em Educação Social (2013/2017) da Universidade de Pablo de Olavide / Sevilha (Espanha). Por meio de metodologia descritiva e qualitativa, codificamos e analizamos as frequências temáticas das nuvens de palavras criadas pelos alunos correspondentes ao ano acadêmico 2011-12 do duplo Grau de Serviço Social e Educação Social (TS / ES). Assim, comparamos com os demais cursos acadêmicos. Resultados. Os resultados mostram que os termos mais representativos para os alunos são: Globalização (61,86%), Comunicação (43,85%), Tecnologia (43,85%), Tecnologias de Informação e Comunicação (TIC) (41,73%), Informação (38,98%) e Internet-Rede de redes (25,21%). Por fim, cabe destacar que dezesseis novos termos aparecem, dependendo do ano acadêmico, sendo os mais mencionados: Desigualdade (19,91%), Crise (13,13%) e Consumo (8,47%). Conclusão. Essa experiência universitária mostra que o uso educacional da criação de conteúdo digital com recursos de nuvens de palavras pode ser um recurso didático muito interessante como veículo de reflexão e repositório de experiências de aprendizagem para estudantes universitários.

Women's perspectives of early menopause: development of a word cloud.

Objective: Early menopause (EM), menopause aged <45 years, occurs spontaneously or secondary to medical treatments and is associated with multiple health impacts. A word cloud is an image where the word size reflects the frequency of use. We aimed to assess the perspectives of women with EM using a word cloud.Methods: Women diagnosed with EM, recruited from clinics/community, completed a survey including the open-ended question 'What words do you associate with EM?'. Demographics and medical history were collected. Data analysis included descriptive statistics, identification of word themes/stems/synonyms, word frequency, and chi-square test. A word cloud was constructed from words used by two or more women using 'Wordle' (www.wordle.net).Results: Responses were obtained from 190/263 participants. The mean age was 54 ± 11 years, with EM diagnosed at age 38 ± 5 years. The cause of EM was unknown (30% of women), bilateral oophorectomy (27%), cancer therapy (25%), or autoimmune/genetic/metabolic (17%). The commonest words reported were hot flushes (36.8% of women), mood swings (20.5%), and infertility (16.8%), which varied with age and cause of EM. Few women reported neutral/positive words.Conclusion: Most words that women associate with EM have negative connotations and refer to symptoms. A word cloud is a novel way to illustrate women's perspectives.