Internet Rumors During the COVID-19 Pandemic: Dynamics of Topics and Public Psychologies.

The capturing of social opinions, especially rumors, is a crucial issue in digital public health. With the outbreak of the COVID-19 pandemic, the discussions of related topics have increased exponentially in social media, with a large number of rumors on the Internet, which highly impede the harmony and sustainable development of society. As human health has never suffered a threat of this magnitude since the Internet era, past studies have lacked in-depth analysis of rumors regarding such a globally sweeping pandemic. This text-based analysis explores the dynamic features of Internet rumors during the COVID-19 pandemic considering the progress of the pandemic as time-series. Specifically, a Latent Dirichlet Allocation (LDA) model is used to extract rumor topics that spread widely during the pandemic, and the extracted six rumor topics, i.e., "Human Immunity," "Technology R&D," "Virus Protection," "People's Livelihood," "Virus Spreading," and "Psychosomatic Health" are found to show a certain degree of concentrated distribution at different stages of the pandemic. Linguistic Inquiry and Word Count (LIWC) is used to statistically test the psychosocial dynamics reflected in the rumor texts, and the results show differences in psychosocial characteristics of rumors at different stages of the pandemic progression. There are also differences in the indicators of psychosocial characteristics between truth and disinformation. Our results reveal which topics of rumors and which psychosocial characteristics are more likely to spread at each stage of progress of the pandemic. The findings contribute to a comprehensive understanding of the changing public opinions and psychological dynamics during the pandemic, and also provide reference for public opinion responses to major public health emergencies that may arise in the future.

Rapid Detection of Anti-SARS-CoV-2 IgM and IgG Using a Selenium Nanoparticle-based Lateral Flow Immunoassay (preprint)

Background: Coronavirus disease 2019 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is highly transmissible. Early and rapid testing is necessary to effectively prevent and control the outbreak. Detection of SARS-CoV-2 antibodies with lateral flow immunoassay can achieve this goal. Antibody detection is especially effective for the detection of asymptomatic infection.Methods: In this study, SARS-CoV-2 nucleoprotein was expressed by E. coli and purified by affinity chromatography. We used the highly stable and sensitive selenium nanoparticle as the labeling probe coupled with the SARS-CoV-2 nucleoprotein to prepare a new SARS-CoV-2 antibody (IgM and IgG) detection kit. The sensitivity and specificity of the kit were verified by plasma of COVID-19 patients and health persons. Separate detection of IgM and IgG, such as in this assay, was performed in order to reduce mutual interference and improve the accuracy of the test results.Results: The SARS-CoV-2 nucleoprotein was purified on a nickel column, and the final purity was greater than 90%. The sensitivity of the kit was 94.74% and the specificity was 95.12% by 41 negative plasma samples and 19 positive plasma samples detection.Conclusions: The assay kit does not require any special device for reading the results and the readout is a simple color change that can be evaluated with the naked eye. This kit is suitable for rapid and real-time detection of the SARS-CoV-2 antibody.