A content analysis of social media on outbreak of infodemic amidst COVID-19 pandemic

Abstract

BACKGROUND: With so much content on social media platforms about COVID-19, determining which information is reliable can be a daunting task. Hence, this study is aimed to analyze various posts with regard to COVID-19 on various social media platforms for their reliability and also examined various factors that influence information reliability. MATERIALS AND METHODS: A cross-sectional study was conducted, with 934 samples related to coronavirus pandemic published on Twitter, Instagram, and Facebook using systematic random sampling. We adopted the criteria given by Paul Bradshaw and modified to assess the characteristics of the samples. Training and calibration of the investigators were carried out for 3 consecutive days before beginning the study. The data were analyzed using the Chi-square test and multinomial logistic regression to estimate the odds ratios. RESULTS: Out of 934 samples studied, only 570 (61%) were found to be reliable of which 243 (42.6%) were from Twitter, 117 (20.6%) from Instagram, and 210 (36.8%) from Facebook. We found that the reliability of the information on social media platforms is significantly influenced by network (odds: 1.32; 95% confidence interval [CI]: 1.16–1.52; P = 0.036), content (odds: 1.83; 95% CI: 1.69–1.92; P = 0.009), contextual update (odds: 1.41; 95% CI: 1.24–1.53) and age of the account (odds: 1.92; 95% CI: 1.64–2.09; P = 0.002). CONCLUSION: Our study shows that the reliability of the social media posts significantly depends on the network, contextual update, and age of the account. Hence, cross verifying the information from a reliable source is the need of the hour to prevent panic and mental distress.