Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

Li, Jiawei; Xu, Qing; Cuomo, Raphael E.; Purushothaman, Vidya; Mackey, Tim K.

doi:10.2196/18700

Cited by 165 publications

(150 citation statements)

References 13 publications

Supporting

Mentioning

139

Contrasting

Unclassified

Order By: Relevance

“…An analysis of German Facebook groups whose discussions center on the pandemic uses a similar named entity analysis to our methods, and shows a strong tendency among the Facebook group members to resist the news reported by recognized journalistic sources [9]. A study of the Chinese social media site Weibo revealed a broad range of concerns from disease origin and progression to reactions to public health initiatives [42]. An examination of 4Chan that employs network analysis techniques and entity rankings traces the emergence of Sino-phobic attitudes on social media, which are echoed in our narrative frameworks [58].…”

Section: Prior Workmentioning

confidence: 99%

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Shahsavari¹,

Holur²,

Wang³

et al. 2020

Preprint

107

View full text Add to dashboard Cite

Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.

show abstract

Section: Prior Workmentioning

confidence: 99%

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Shahsavari¹,

Holur²,

Wang³

et al. 2020

Preprint

107

View full text Add to dashboard Cite

show abstract

“…An analysis of German Facebook groups whose discussions center on the pandemic uses a similar named entity analysis to our methods, and shows a strong tendency among the Facebook group members to resist the news reported by recognized journalistic sources [ 9 ]. A study of the Chinese social media site Weibo revealed a broad range of concerns from disease origin and progression to reactions to public health initiatives [ 42 ]. An examination of 4Chan that employs network analysis techniques and entity rankings traces the emergence of Sino-phobic attitudes on social media; these attitudes are echoed in our narrative frameworks [ 58 ].…”

Section: Prior Workmentioning

confidence: 99%

Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news

Shahsavari¹,

Holur²,

Wang³

et al. 2020

J Comput Soc Sc

175

View full text Add to dashboard Cite

Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the COVID-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long-term social and economic ramifications of the pandemic. Among the stories currently circulating in US-focused social media forums are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frameworks supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.

show abstract

“…In another study by Li et al [ 15 ], the daily trend data related to specific keyword search such as “coronavirus” and “pneumonia”, has been acquired from Google Trends, Baidu Index, and Sina Weibo Index search engines to investigate and monitor new COVID-19 cases. Li et al [ 16 ] have collected data on the posts related to COVID-19 that are posted by Chinese users on Weibo using an automated Python programming script. The collected data have been analyzed quantitatively and qualitatively in order to recognize trends and characterize key themes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, Hernandez-Matamoros et al [ 13 ] have developed an ARIMA model to predict the spread of the virus, while considering some factors like the population and the number of infected cases. There are other studies that focus on collecting and analyzing posts related to COVID-19 from social media sites [ 14 , 15 , 16 ]. This is because the keyword search trends related to COVID-19 on search engines proved to be tremendously helpful in predicting and monitoring the spread of the virus outbreak.…”

Section: Introductionmentioning

confidence: 99%

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Eltoukhy

Shaban

Chan

et al. 2020

IJERPH

View full text Add to dashboard Cite

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

show abstract

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

Cited by 165 publications

References 13 publications

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Contact Info

Product

Resources

About