Similarity Analysis in Understanding Online News in Response to Public Health Crisis

Cezario, Sidemar Fideles; Marques, Thiago; Pinto, Rafael; Lacerda, Juciano de Sousa; Silva, Lyrene Fernandes da; Lima, Thaisa Santos; Santana, Orivaldo; Ribeiro, Anna G. C. D.; Cruz, Agnaldo S.; Araújo, Ana Cláudia Costa de; Miranda, Angélica Espinosa; Cadaxa, Aedê; Teixeira, César; Gallego, Almudena Muñoz; Valentim, Ricardo Alexsandro de Medeiros

doi:10.3390/ijerph192417049

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…The study of the similarity of some entities is increasingly used in research from all fields: medical, public health, education, management, environment, business, crisis events/situations in society, communication. Through the computational determination of the similarity of some texts and some news transmitted online regarding a special event or situation, it is possible to evaluate the effectiveness of communication actions in influencing society's behavior regarding public health [72], to achieve the semantic grouping of documents [73], to analyze the similarity of the weighting methods of the criteria taken into account in Multicriteria Decision Support Systems in management [74], and to study cerebral activity on several channels in medicine [75].…”

Section: Sentiment Analysis Using Python's Open-source Textblob Nltk ...mentioning

confidence: 99%

Content Analysis Using Specific Natural Language Processing Methods for Big Data

Pirnau,

Botezatu,

Priescu

et al. 2024

Electronics

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Researchers from different fields have studied the effects of the COVID-19 pandemic and published their results in peer-reviewed journals indexed in international databases such as Web of Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating the extensive literature on COVID-19 pandemic research, our study conducts a content analysis of the top 1000 cited papers in WoS that delve into the subject by using elements of natural language processing (NLP). Knowing that in WoS, a scientific paper is described by the group Paper = {Abstract, Keyword, Title}; we obtained via NLP methods the word dictionaries with their frequencies of use and the word cloud for the 100 most used words, and we investigated if there is a degree of similarity between the titles of the papers and their abstracts, respectively. Using the Python packages NLTK, TextBlob, VADER, we computed sentiment scores for paper titles and abstracts, analyzed the results, and then, using Azure Machine Learning-Sentiment analysis, extended the range of comparison of sentiment scores. Our proposed analysis method can be applied to any research topic or theme from papers, articles, or projects in various fields of specialization to create a minimal dictionary of terms based on frequency of use, with visual representation by word cloud. Complementing the content analysis in our research with sentiment and similarity analysis highlights the different or similar treatment of the topics addressed in the research, as well as the opinions and feelings conveyed by the authors in relation to the researched issue.

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Section: Sentiment Analysis Using Python's Open-source Textblob Nltk ...mentioning

confidence: 99%

Content Analysis Using Specific Natural Language Processing Methods for Big Data

Pirnau,

Botezatu,

Priescu

et al. 2024

Electronics

View full text Add to dashboard Cite

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Text mining analysis to understand the impact of online news on public health response: case of syphilis epidemic in Brazil

Pinto,

Lacerda,

Silva

et al. 2023

Front. Public Health

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BackgroundTo effectively combat the rising incidence of syphilis, the Brazilian Ministry of Health (MoH) created a National Rapid Response to Syphilis with actions aimed at bolstering epidemiological surveillance of acquired, congenital syphilis, and syphilis during pregnancy complemented with communication activities to raise population awareness and to increase uptake of testing that targeted mass media outlets from November 2018 to March 2019 throughout Brazil, and mainly areas with high rates of syphilis. This study analyzes the volume and quality of online news content on syphilis in Brazil between 2015 and 2019 and examines its effect on testing.MethodsThe collection and processing of online news were automated by means of a proprietary digital health ecosystem established for the study. We applied text data mining techniques to online news to extract patterns from categories of text. The presence and combination of such categories in collected texts determined the quality of news that were analyzed to classify them as high-, medium-and low-quality news. We examined the correlation between the quality of news and the volume of syphilis testing using Spearman’s Rank Correlation Coefficient.Results1,049 web pages were collected using a Google Search API, of which 630 were categorized as earned media. We observed a steady increase in the number of news on syphilis in 2015 (n = 18), 2016 (n = 26), and 2017 (n = 42), with a substantial rise in the number of news in 2018 (n = 107) and 2019 (n = 437), although the relative proportion of high-quality news remained consistently high (77.6 and 70.5% respectively) and in line with similar years. We found a correlation between news quality and syphilis testing performed in primary health care with an increase of 82.32, 78.13, and 73.20%, respectively, in the three types of treponemal tests used to confirm an infection.ConclusionEffective communication strategies that lead to dissemination of high quality of information are important to increase uptake of public health policy actions.

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Similarity Analysis in Understanding Online News in Response to Public Health Crisis

Cited by 2 publications

References 34 publications

Content Analysis Using Specific Natural Language Processing Methods for Big Data

Content Analysis Using Specific Natural Language Processing Methods for Big Data

Text mining analysis to understand the impact of online news on public health response: case of syphilis epidemic in Brazil

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