Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis

Abstract

Background In case of a population-wide infectious disease outbreak, such as the novel coronavirus disease (COVID-19), people’s online activities could significantly affect public concerns and health behaviors due to difficulty in accessing credible information from reliable sources, which in turn causes people to seek necessary information on the web. Therefore, measuring and analyzing online health communication and public sentiment is essential for establishing effective and efficient disease control policies, especially in the early stage of an outbreak. Objective This study aimed to investigate the trends of online health communication, analyze the focus of people’s anxiety in the early stages of COVID-19, and evaluate the appropriateness of online information. Methods We collected 13,148 questions and 29,040 answers related to COVID-19 from Naver, the most popular Korean web portal (January 20, 2020, to March 2, 2020). Three main methods were used in this study: (1) the structural topic model was used to examine the topics in the online questions; (2) word network analysis was conducted to analyze the focus of people’s anxiety and worry in the questions; and (3) two medical doctors assessed the appropriateness of the answers to the questions, which were primarily related to people’s anxiety. Results A total of 50 topics and 6 cohesive topic communities were identified from the questions. Among them, topic community 4 (suspecting COVID-19 infection after developing a particular symptom) accounted for the largest portion of the questions. As the number of confirmed patients increased, the proportion of topics belonging to topic community 4 also increased. Additionally, the prolonged situation led to a slight increase in the proportion of topics related to job issues. People’s anxieties and worries were closely related with physical symptoms and self-protection methods. Although relatively appropriate to suspect physical symptoms, a high proportion of answers related to self-protection methods were assessed as misinformation or advertisements. Conclusions Search activity for online information regarding the COVID-19 outbreak has been active. Many of the online questions were related to people’s anxieties and worries. A considerable portion of corresponding answers had false information or were advertisements. The study results could contribute reference information to various countries that need to monitor public anxiety and provide appropriate information in the early stage of an infectious disease outbreak, including COVID-19. Our research also contributes to developing methods for measuring public opinion and sentiment in an epidemic situation based on natural language data on the internet.

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

“…Online news is a hotbed of negativity and drives negative sentiment and blame in other media. The stigmatizing terms were clearly deemed offensive, and they might have contributed to recent backlashes against China and Chinese people by encouraging and directing blame [ 6 - 8 ]. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount while considering the online public’s responses and feelings around making biased judgments.…”

“…Social stigma in the context of a disease outbreak comes from an impulse to assign blame; hence, abundant research has acknowledged the social stigma and the subsequent blame and discrimination attached to COVID-19 (eg, [ 6 - 8 ]). The lack of a clear understanding about social stigma regarding the COVID-19 pandemic may lead to the circulation of false blame and negative bias, which jeopardizes the public’s psychosocial development and well-being.…”

“…The naming of health risks for specific groups can fuel the negative consequences that occur as a result of the fight against infectious diseases. Therefore, researchers have confirmed the widespread collective perceptual bias against the Chinese by using stigmatizing monikers [ 8 ].…”

Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

“…In addition, web-based social question-and-answer forums have been analyzed to identify topic communities and asses the appropriateness of the answers during the early stage of the COVID-19 outbreak [ 10 ]. Other studies have analyzed Twitter data to understand the impact of COVID-19 on specific public attitudes and behaviors, such as xenophobia [ 11 ] and the spread of the 5G conspiracy theory [ 12 ].…”

Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data

“…For instance Ref. [ 13 ], proposed an LDA short text clustering algorithm based on sentiment word co-occurrence and knowledge pair feature extraction; the experimental results show that it reveals better semantic analysis ability and emotional topic clustering effect [ 14 ]. Collected questions and answers related to COVID-19 from Naver and then used the structural topic model and word network analysis to analyze the focus of people's anxiety and worry.…”