ABSTRACT
O presente artigo busca analisar postagens na rede social digital Twitter que contêm os termos 'HIV/aids' e 'covid-19' publicadas em abril de 2021, quando o Ministério da Saúde amplia a vacinação contra a covid-19 para pessoas com HIV/aids. Nosso objetivo foi o de comparar os dois acontecimentos epidemiológicos do país, evidenciar paralelos, subjetividades e lições a partir do corpus. Para tanto, optamos por um método quantiqualitativo de análise de redes semânticas baseada na coleta de conteúdos digitais, identificandose os pares ou o conjunto de palavras que mais se conectam, formando redes de significações análogas, denominadas clusters. Como resultado, identificamos a polarização político-partidária dos comentários sobre covid-19 e HIV/aids no Twitter, a reemergência dos estigmas associados a grupos específicos, como de homossexuais e asiáticos, o espalhamento em larga escala de desinformação sobre as duas doenças, revelando um campo de tensões e de disputas narrativas e midiáticas como ferramenta 'necropolítica'.
This article seeks to analyze posts on the digital social network Twitter containing the terms 'HIV/aids' and 'covid-19' published in April 2021, when the Ministry of Health expands vaccination against covid-19 for people with HIV/aids. Our objective was to compare the two epidemiological events in the country, highlighting parallels, subjectivities and lessons from the corpus. In order to do that, we chose a quanti-qualitative method of analysis of semantic networks based on the collection of digital content, identifying the pairs or sets of words that most connect, forming networks of analogous meanings, called clusters. As a result, we identified the political-partisan polarization of comments on covid-19 and HIV/aids on Twitter, the re-emergence of stigmas associated with specific groups, such as homosexuals and Asians, the largescale spread of misinformation about the two diseases, revealing a field of tensions and narrative and media disputes as a 'necropolitical' tool.
Este artículo busca analizar publicaciones em la red social digital Twitter que contienen los términos 'VIH/sida' y 'covid-19' publicados en abril de 2021, cuando el Ministerio de Salud amplía la vacunación contra covid-19 para personas con VIH/sida. Nuestro objetivo fue comparar los dos eventos epidemiológicos en el país, destacando paralelos, subjetividades y lecciones del corpus. Por ello, optamos por un método cuanticualitativo de análisis de redes semánticas basado en la recolección de contenido digital, identificando los pares o conjuntos de palabras que más conectan, formando redes de significados análogos, llamados clusters. Como resultado, identificamos la polarización político-partidista de los comentarios sobre el covid-19 y el VIH/sida en Twitter, el resurgimiento de estigmas asociados con grupos específicos, como los homosexuales y los asiáticos, la difusión a gran escala de información errónea sobre las dos enfermedades, revelando un campo de tensiones y disputas narrativas y mediáticas como herramienta 'necropolítica'.
Subject(s)
Humans , HIV , Communication , Social Media , Semantic Web , COVID-19/immunology , Vaccination , Access to Information , Social DiscriminationABSTRACT
Objective:The topic model is a well-known method used in the field of natural language processing (NLP)that defines adocument as constructed of topics that combine specific t erms. This method is used to model topic co-occurrencemathematically. In this study,we extracted topics from featu re vectors of explicit documents called medical package insertsby using cluster analysis. Methods:We counted the terms(nouns)recognized by the morphological analysis engine MeCab and created a documentterm matrix. A value of“tf・idf”was calculated in this matrix for term weighting to avoid the effect of term frequency. We reduced the dimensionality of the matrix using singular v alue decomposition,which removed unnecessary data,and weextracted feature vectors attributed to each medical package insert. The distance between feature vectors was calculatedusing cosine distance,and cluster analysis was performed based on the distance between the vectors.Results:Cluster analysis on our document-term matrix show ed that medical package inserts of drugs that have the sameefficacy or active ingredient were included in the same cl uster. Moreover, using term weighting and dimensionalityreduction,we could extract topics from medical package inserts.Conclusion:We obtained a foothold to apply our findings t o the recommendation of similar drugs. Cluster analysis ofmedical package inserts using NLP can contribute to the pro per application of drugs. In addition,our study revealed thesimilarities of drugs and suggested possibilities for new applications from several points of view.
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Co-citations of foreign and domestic highly cited papers on drug target discovery were analyzed by clustering analysis using BICOMB2.01 and gCLUTO.Semantic analysis of the titles and abstracts in these highly cited papers and their important source literature showed that general trend, theoretical foundation, main methods and principal resources are the major hotspots of text mining in drug target discovery.
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Objective To discuss the use of knowledge graph technology to connect various trivial and fragmented knowledge in various medical information systems and support comprehensive knowledge retrieval and intelligent medical applications such as Q & A and clinical decision support.Methods Based on the construction of medical field ontology and semantic labeling of medical knowledge base,the medical knowledge graph was constructed and applied to intelligent medicine,with chronic disease taken as an example.Results The construction method of medical knowledge graph and its application in semantic analysis,reasoning and disease-assisted diagnosis system based on medical knowledge base were put forward.Conclusion The application of intelligent medicine based on knowledge graph will play an important role in contradiction between the supply of high-quality medical resources and increasing medical requirements.
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A semantic predication network was developed by processing the documents on schizophrenia into se-mantic predication sets using SemRep, from which the core information was extracted to produce a graphic summary which was consisted of highly cohesive cliques.The automatic methods for summarizing biomedical documents were studied using the network properties combined with semantic information.The subthemes in the summary obtained by clustering were evaluated according to the clique co-node matrix and the contents of the summary were assessed according to the reference criteria.The accuracy was 0.93, the recall was 0.68, and the F-value was 0.79 for the summary, indicating that this method can effectively recognize the core information in documents and the semantic information in network graphics.
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RESUMO Magical Ideation Scale (MIS) é uma escala de autorrelato que avalia pensamentos mágicos, fenômeno ligado à esquizofrenia. Este trabalho procurou adaptar a MIS à cultura brasileira, bem como investigar sua sensibilidade discriminativa a partir de um estudo de validade. Na etapa de adaptação, 283 sujeitos responderam a MIS, marcando palavras e itens incompreendidos. Posteriormente, administrou-se a escala a 70 sujeitos divididos em grupos: Pacientes e Não-pacientes. A comparação do desempenho de ambos foi obtida por meio do teste t de Student, revelando diferença significativa e de acentuada magnitude, conforme d de Cohen. A correlação de Pearson entre o diagnóstico de esquizofrenia e a MIS evidenciou associação positiva de expressiva magnitude. Interpretam-se os resultados como evidências de validade para a MIS.
ABSTRACT Magical Ideation Scale (MIS) is a self-report scale that assesses magical thinking, a phenomenon linked to schizophrenia. This study aimed to adapt the MIS to Brazilian culture, as well as investigate its discriminative sensitivity based on a validity study. In the adaptation stage, 283 subjects completed MIS, marking words and misunderstood items. Subsequently, the scale was administered to 70 subjects divided into groups: Patients and Non-patients. A comparison of the performance of both groups was realized using Student’s t, which revealed significant differences, while Cohen’s d showed a strong discrepancy between the two groups. The Pearson correlation between the diagnosis of schizophrenia and MIS showed a positive association of significant magnitude. The results are interpreted as evidence of validity for the MIS.
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Objective To explore the way to compile a global military medical literature collection and perspectives for its research.Methods The names of military medical research institutions and their trends of development were summa-rized via semantic analysis.The collection of retrieval words for military medical research institutions( then the collection) was constructed based on expert consultation.The names of military medical research institutions were collect-ed with manual screening after retrieval withthe collection.The literature collection of military medical research institu-tions was completed with coordinated retrieved of their papers and other publications.Results According to different needs of information analysis, the literature collection of military medical research institutions could be analyzed in terms of their size, types of development, and academic authority.Conclusion Based onthe collection, the military medical research institutions collected in this article included institutions that used to be neglected during the course of information tracking of military medicine.Three kinds of institutions should be paid more attention to.The institutions were the ones with a large number of papers and citations, the ones whose papers increased or decreased dramatically, as well as the ones whose research directions were the priority fields of Chinese PLA.
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El objetivo de este trabajo es identificar un método de evaluación automática de resúmenes realizados a partir de textos de tipo narrativo y expositivo en español. Para llevar a cabo esta tarea se correlaciona la evaluación realizada por tres docentes a 373 resúmenes con los resultados entregados por el análisis semántico latente. Los puntajes asignados por el análisis semántico latente se obtienen utilizando tres métodos 1) Comparación de los resúmenes con el texto fuente, 2) Comparación de los resúmenes con un resumen consensuado 3) Comparación de los resúmenes con tres resúmenes construidos por tres evaluadores. Entre los resultados más relevantes se destacan: a) una alta correlación entre la evaluación realizada por los evaluadores ( 0,63); b) una alta correlación entre los métodos computacionales utilizados ( 0,62) y c) una correlación promedio positiva media-alta entre las evaluaciones realizadas por los docentes y el análisis semántico latente en el segundo y tercer método ( 0,53 en ambos casos y tipos de textos). Ambos métodos presentaron mayor correlación promedio con los evaluadores cuando los textos evaluados eran predominantemente narrativos ( 0,59 y 0,45 respectivamente).
The objective of this study is to identify a method for the automatic evaluation of the summaries developed from narrative and expository Spanish texts. In order to fulfill this task evaluation of 373 summaries carried out by three teachers is correlated with the results delivered by latent semantic analysis. Scores assigned by the latent semanticanalysis are obtained through three methods: 1) Comparison of the summaries with the source text, 2) Comparison of the summaries with a consensuated one, 3) Comparison of the summaries with three summaries developed by three evaluators. The mostrelevant results include: a) a high correlation between assessments by the evaluators (:0.63), b) a high correlation between the computational methods used (:0.62) and c) a positive medium-high average correlation between assessments undertaken bythe teachers and the latent semantic analysis in the second and third method (;0.53 in both cases and types of texts). Both methods presented greater average correlation with testers when the texts evaluated were predominantly narratives (;0.59 and 0.45 respectively).