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Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science research has played a key role in policymaking during this global crisis. This study addresses two key research questions: How FAIR ( findable, accessible, interoperable and reusable) are social science data (...) on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n = 1131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019 and 2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n = 45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research. (shrink) | |
Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.,The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.,The authors found eight emotions, with negativity outweighing positivity, in (...) the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.,The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.,This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling. (shrink) No categories |
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