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In this study, we introduce a novel metric called the Apex index, which is a hit-based measure for assessing the impact of scientific publications and scientists. The basic principle of the Apex index involves quantifying the number of hit papers that cite the focal paper. Specifically, we identify the top 1% most highly cited papers in a given field and year as the hit papers. We then calculate the Apex index for all publications in the MAG database, which contains approximately 200 million documents. Our study reveals that Nobel Prize-winning papers display a higher Apex index compared to other papers, and Nobel laureates exhibit a higher Apex index than their peers. Moreover, we demonstrate that the Apex index has a higher convergent validity in evaluating scientists and identifying laureates. Overall, the Apex index presents a valuable and effective tool for assessing the impact of scientific publications and researchers.

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BACKGROUND During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. OBJECTIVE We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media. METHODS The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. RESULTS Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination. CONCLUSIONS Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.