Content characteristics predict the putative authenticity of COVID-19 rumors.

Rumors regarding COVID-19 have been prevalent on the Internet and affect the control of the COVID-19 pandemic. Using 1,296 COVID-19 rumors collected from an online platform (piyao.org.cn) in China, we found measurable differences in the content characteristics between true and false rumors. We revealed that the length of a rumor's headline is negatively related to the probability of a rumor being true [odds ratio (OR) = 0.37, 95% CI (0.30, 0.44)]. In contrast, the length of a rumor's statement is positively related to this probability [OR = 1.11, 95% CI (1.09, 1.13)]. In addition, we found that a rumor is more likely to be true if it contains concrete places [OR = 20.83, 95% CI (9.60, 48.98)] and it specifies the date or time of events [OR = 22.31, 95% CI (9.63, 57.92)]. The rumor is also likely to be true when it does not evoke positive or negative emotions [OR = 0.15, 95% CI (0.08, 0.29)] and does not include a call for action [OR = 0.06, 95% CI (0.02, 0.12)]. By contrast, the presence of source cues [OR = 0.64, 95% CI (0.31, 1.28)] and visuals [OR = 1.41, 95% CI (0.53, 3.73)] is related to this probability with limited significance. Our findings provide some clues for identifying COVID-19 rumors using their content characteristics.

Value of the Neutrophil-Lymphocyte Ratio in Predicting COVID-19 Severity: A Meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is highly contagious and continues to spread rapidly. However, there are no simple and timely laboratory techniques to determine the severity of COVID-19. In this meta-analysis, we assessed the potential of the neutrophil-lymphocyte ratio (NLR) as an indicator of severe versus nonsevere COVID-19 cases. METHODS: A search for studies on the NLR in severe and nonsevere COVID-19 cases published from January 1, 2020, to July 1, 2021, was conducted on the PubMed, EMBASE, and Cochrane Library databases. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and area under the curve (AUC) analyses were done on Stata 14.0 and Meta-disc 1.4 to assess the performance of the NLR. RESULTS: Thirty studies, including 5570 patients, were analyzed. Of these, 1603 and 3967 patients had severe and nonsevere COVID-19, respectively. The overall sensitivity and specificity were 0.82 (95% confidence interval (CI), 0.77-0.87) and 0.77 (95% CI, 0.70-0.83), respectively; positive and negative correlation ratios were 3.6 (95% CI, 2.7-4.7) and 0.23 (95% CI, 0.17-0.30), respectively; DOR was 16 (95% CI, 10-24), and the AUC was 0.87 (95% CI, 0.84-0.90). CONCLUSION: The NLR could accurately determine the severity of COVID-19 and can be used to identify patients with severe disease to guide clinical decision-making.