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

Abstract

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.

Characteristics of Collective Resilience and Its Influencing Factors from the Perspective of Psychological Emotion: A Case Study of COVID-19 in China

“…Since the outbreak of COVID-19, the extraction of the semantic or emotional content of textual commentaries posted by the public has become a useful research tool to explore the evolution of topics and the emotional expression characteristics of particular groups Due to the prolonged series of COVID-19 lockdowns, social media has also become a placeholder for collective resilient processes modulated by cognitive and emotional components [ 42 ]. This article also draws on a similar research approach, using Weibo data and Baidu indexes, to investigate the collective resilience characteristics of social groups during an emergent public crisis, taking randomly-selected unstructured groups with different social attributesfrom the Weibo platform as research subjects [ 25 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ].…”

FSRD:Few-shot fuzzy rumor detection system with stance-enhanced prompt learning

“…Since the outbreak of the Covid-19, many rumors about it have emerged, such as "drinking soda to prevent new coronaviruses", "playing in the snow to catch new coronaviruses" [4]. A large portion of this misinformation exacerbates the public's physical overreaction and psychological fear of unknown viruses [5,6]. Therefore, identifying and suppressing the spread of rumors is crucial to eliminate such adverse public opinion events.…”

Analysis of an optimal control problem for a spatio‐temporal SIR model with nonlinear density dependent diffusion terms

“…Furthermore, for populations who exhibit herd instinct, that is, the behavior wherein people join groups and follow the actions of others, when an individual observes that the confinement measures, restricting the movement of individuals, are not being respected by others, they tend to adopt the same behavior. This can also happen due to the propagation of false rumors, indicating that the disease is not infectious or not dangerous enough to impose highly restrictive measures 27 . Taking the previous discussion into account, in order to increase the pertinence of epidemic models and their interpretation of reality, it is of great interest to consider spatio‐temporal models, in which the spatial diffusion rates corresponding to the susceptible, infected and recovered populations depend on the current state of the total population.…”

“…This can also happen due to the propagation of false rumors, indicating that the disease is not infectious or not dangerous enough to impose highly restrictive measures. 27 Taking the previous discussion into account, in order to increase the pertinence of epidemic models and their interpretation of reality, it is of great interest to consider spatio-temporal models, in which the spatial diffusion rates corresponding to the susceptible, infected and recovered populations depend on the current state of the total population.…”