A global health crisis with divided research traditions? A comparative review of Brazilian and international research in communication on the COVID-19 pandemic
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Cited by 3 publications
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Optimized discovery of discourse topics in social media: science communication about COVID-19 in Brazil
PurposeSocial media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.Design/methodology/approachTo collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.FindingsWe provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.Research limitations/implicationsThe challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.Practical implicationsWith this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.Originality/valueThis study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.
Optimized discovery of discourse topics in social media: science communication about COVID-19 in Brazil
PurposeSocial media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.Design/methodology/approachTo collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.FindingsWe provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.Research limitations/implicationsThe challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.Practical implicationsWith this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.Originality/valueThis study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.
Moving from Offline to Online: How COVID-19 Affected Research in the Social and Behavioral Sciences
During the COVID-19 pandemic, researchers have faced a lot of challenges related to their daily work. This article introduces a special issue of the American Behavioral Scientist, which particularly focuses on methodological challenges caused by the COVID-19 pandemic. Based on a brief review of the literature as well as the studies in this issue, we argue that the pandemic has sparked significant methodological innovations with respect to design, data collection, study documentation, and scholarly collaboration. We distinguish two types of innovations, both conceptualized as the outcome of an unprecedented external shock. First, “methodological compromises” that enabled data collection during the pandemic, but are inferior to established approaches. These methodological compromises, therefore, may be abandoned in post-pandemic times. Second, there are also “methodological game changers” that are superior to classic approaches and thus may prevail in the long run. Regardless of the type, we call scholars in the social and behavioral sciences to systematically test, compare, and evaluate the methodological innovations brought to us as a result of the COVID-19 pandemic.
Redefining searching in non-medical sciences systematic reviews: The ascendance of Google Scholar as the primary database
Systematic Reviews (SR) are fundamental in evidence-based medicine, traditionally dominated by the medical field. However, their application in non-medical disciplines especially social sciences is increasing. Compared to traditional databases this study explores the feasibility of using Google Scholar as the primary search index for SRs in social sciences. An empirical comparative analysis was conducted on 16 Non-Medical Sciences Systematic Reviews (NMSSR), seven of which are published; all others are in progress or in submission, comparing article yields from traditional databases and advanced Google Scholar searching. Traditional databases in this study yielded 70,011 after deduplication (136,277 before deduplication) while advanced Google Scholar searches yielded 50,793 articles. All articles included in the final datasets of the 16 SRs (1116) were found in the advanced Google Scholar searches, indicating its comprehensive coverage. This suggests that Google Scholar can potentially serve as the primary database for NMSSRs, offering comprehensive coverage by reducing database discrepancies, and duplicated resources while advancing transparency and reproducibility. Challenges with search precision and download limitations warrant consideration for its optimal use in systematic reviews.
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