Data-driven analytics of COVID-19 ‘infodemic’

Abstract

The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on fact-checking algorithms or multi-class labeling models that are less aware of the intrinsic characteristics of the language. Nor is it discussed how such representations can account for the common psycho-socio-behavior of the information consumers. This work takes a data-driven analytical approach to (1) describe the prominent lexical and grammatical features of COVID-19 misinformation; (2) interpret the underlying (psycho-)linguistic triggers in terms of sentiment, power and activity based on the affective control theory; (3) study the feature indexing for anti-infodemic modeling. The results show distinct language generalization patterns of misinformation of favoring evaluative terms and multimedia devices in delivering a negative sentiment. Such appeals are effective to arouse people's sympathy toward the vulnerable community and foment their spreading behavior.

Generative artificial intelligence can have a role in combating vaccine hesitancy

“…Its multifaceted and temporal nature makes it a moving target, challenging to predict and harder to tackle. Additionally, the emergence of misinformation in public health, notably during crises such as the covid-19 pandemic, calls for rapid, data driven responses 4…”

“…Additionally, the emergence of misinformation in public health, notably during crises such as the covid-19 pandemic, calls for rapid, data driven responses. 4 …”

A comparative analysis of the COVID-19 Infodemic in English and Chinese: insights from social media textual data

“…Gupta et al ( 10 ) identified topics and key themes present in English COVID-19 fake and real news, compared the emotions associated with these records and gained an understanding of the network-oriented characteristics embedded within them. Wan et al ( 11 ) described the prominent lexical and grammatical features of English COVID-19 misinformation, interpreted the underlying (psycho-)linguistic triggers, and studied the feature indexing for anti-infodemic modeling. Zhao et al ( 12 ) used 1,296 COVID-19 rumors collected from an online platform in China, and found measurable differences in the content characteristics between true and false rumors.…”