Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Park, Han Woo; Park, Sejung; Chong, Miyoung

doi:10.2196/18897

Cited by 229 publications

(228 citation statements)

References 40 publications

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“…The reasons for this choice are twofold. First, many studies have already characterized public response during current and past health emergencies through the lens of Twitter [ 25 , 58 , 60 , 85 , 86 , 103 , 104 ]. Second, several studies have reported a high prevalence of bots as drivers of low-quality information and discussions on COVID-19 on this platform [ 24 , 25 , 105 - 107 ].…”

Section: Discussionmentioning

confidence: 99%

Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

Gozzi¹,

Tizzani²,

Starnini³

et al. 2020

J Med Internet Res

137

124

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Background The exposure and consumption of information during epidemic outbreaks may alter people’s risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19–related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users’ collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people’s collective awareness and risk perception and thus on their tendencies toward behavioral change.

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Section: Discussionmentioning

confidence: 99%

Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

Gozzi¹,

Tizzani²,

Starnini³

et al. 2020

J Med Internet Res

137

124

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“…Themes of previous studies that focus on exploration of, description of, correlation of, or predictive modeling with Twitter data during COVID-19 pandemic include sentiment analysis [17,[25][26][27][28], public attitude/interest measurement [21,[29][30][31], content analysis [15,[32][33][34][35][36], topic modeling [16,26,27,[37][38][39][40], analysis of misinformation, disinformation, or conspiracies [20,[41][42][43][44][45][46], outbreak detection or disease nowcasting/forecasting [18,19], and more [47][48][49][50][51][52]. Similarly, data from other social media channels (e.g., Weibo, Reddit, Facebook) or search engine statistics are utilized for parallel analyses related to COVID-19 pandemic as well [53][54][55][56][57][58][59][60][61]…”

Section: Going Beyond Correlationsmentioning

confidence: 99%

Causal Modeling of Twitter Activity during COVID-19

Gencoglu

Gruber

2020

Computation

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Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g., number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.

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“…Creating tools to harness this pre-existing information to investigate new ideas can be considered an implementation of big data research, which is a growing area of interest [11]. Some areas of big data research include sourcing data from social media [12] and YouTube videos [13]. More recently, researchers have focused on using big data information to evaluate trends in the current COVID-19 pandemic [12], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…Some areas of big data research include sourcing data from social media [12] and YouTube videos [13]. More recently, researchers have focused on using big data information to evaluate trends in the current COVID-19 pandemic [12], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Novel Coronavirus Cough Database: NoCoCoDa

2020

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The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This paper introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media interviews with COVID-19 patients, as an interim solution. After manual segmentation of the interviews, a total of 73 individual cough events were extracted and cough phase annotation was performed. Furthermore, the COVID-19 cough is typically dry but can present as a more productive cough in severe cases. Therefore, an investigation of cough sub-type (productive vs. dry) of the NoCoCoDa was performed using methods previously published by our research group. Most of the NoCoCoDa cough events were recorded either during or after a severe period of the disease, which is supported by the fact that 77% of the COVID-19 coughs were classified as productive based on our previous work. The NoCoCoDa is designed to be used for rapid exploration and algorithm development, which can then be applied to more extensive datasets and potentially real time applications. The NoCoCoDa is available for free to the research community upon request. INDEX TERMS Acoustic signal processing, audio databases, audio systems, biomedical measurement, biomedical monitoring, data analysis, data collection, medical conditions, medical diagnosis, patient monitoring, smart homes.

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Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Cited by 229 publications

References 40 publications

Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

Causal Modeling of Twitter Activity during COVID-19

Novel Coronavirus Cough Database: NoCoCoDa

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