Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China

Lian, Ying; Liu, Yijun; Dong, Xuefan

doi:10.1016/j.techsoc.2020.101265

Cited by 29 publications

(16 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The number of people affected was close to 3 million, with~1200 houses damaged and~174.4 thousand hectares of crops being affected. The direct economic loss exceeded CNY 5.2 billion (USD 77.5 million) [11].…”

Section: Introductionmentioning

confidence: 99%

Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific

et al. 2020

View full text Add to dashboard Cite

Typhoons or mature tropical cyclones (TCs) can affect inland areas of up to hundreds of kilometers with heavy rains and strong winds, along with landslides causing numerous casualties and property damage due to concentrated precipitation over short time periods. To reduce these damages, it is necessary to accurately predict the rainfall induced by TCs in the western North Pacific Region. However, despite dramatic advances in observation and numerical modeling, the accuracy of prediction of typhoon-induced rainfall and spatial distribution remains limited. The present study offers a statistical approach to predicting the accumulated rainfall associated with typhoons based on a historical storm track and intensity data along with observed rainfall data for 55 typhoons affecting the southeastern coastal areas of China from 1961 to 2017. This approach is shown to provide an average root mean square error of 51.2 mm across 75 meteorological stations in the southeast coastal area of China (ranging from 15.8 to 87.3 mm). Moreover, the error is less than 70 mm for most stations, and significantly lower in the three verification cases, thus demonstrating the feasibility of this approach. Furthermore, the use of fuzzy C-means clustering, ensemble averaging, and corrections to typhoon intensities, can provide more accurate rainfall predictions from the method applied herein, thus allowing for improvements to disaster preparedness and emergency response.

show abstract

Section: Introductionmentioning

confidence: 99%

Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific

et al. 2020

View full text Add to dashboard Cite

show abstract

“… From the perspective of external factors, the G-SCNDR online rumor reversal model refines and systematically compares the control strategies of online rumors and finally determines the adoption of the IC strategy as the external intervention approach after comparing the specific measures of external intervention in the current online rumor dissemination model. The isolation-conversion strategy has a lower cost ( Zhao et al., 2019 ) than the traditional debunking strategy ( Jiang, Gao, & Zhuang, 2021 ; Tian, & Ding, 2019 ) and isolation strategy ( Lian, Liu, & Dong, 2020 ), which can promote the spontaneous adoption of disinformation by online users while disclosing refutation information. The study also provides a detailed description and analysis of the process and principles of adopting external interventions, which remedies the ambiguity of the previous descriptions of external interventions.…”

Section: Discussionmentioning

confidence: 99%

“…There are also studies analyzing the influence of debunking behavior and total population change on rumor spreading dynamics ( Tian & Ding, 2019 ). Some studies compare the rumor control efficiency of debunking and isolation strategies through simulation ( Lian, Liu, & Dong, 2020 ). Social reinforcement is also an external influencing factor of online rumor spreading in heterogeneous networks ( Huo & Chen, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A rumor reversal model of online health information during the Covid-19 epidemic

Wang

et al. 2021

Information Processing & Management

View full text Add to dashboard Cite

The development of the Internet and social media has expanded the speed and scope of information dissemination, but not all widely disseminated information is true. Especially during the public health emergencies, the endogenous health information demand generated by the lack of scientific knowledge of health information among online users stimulates the dissemination of health information by mass media while providing opportunities for rumor mongers to publish and spread online rumors. Invalid scientific knowledge and rumors will have a serious negative impact and disrupt social order during epidemic outbreaks such as COVID-19. Therefore, it is extremely important to construct an effective online rumor reversal model. The purpose of this study is to build an online rumor reversal model to control the spread of online rumors and reduce their negative impact. From the perspective of internal and external factors, based on the SIR model, this study constructed a G-SCNDR online rumor reversal model by adopting scientific knowledge level theory and an external online rumor control strategy. In this study, the G-SCNDR model is simulated, and a sensitivity analysis of the important parameters of the model is performed. The reversal efficiency of the G-SCNDR model can be improved by properly adopting the isolation-conversion strategy as the external control approach to online rumors with improving the popularization rate of the level of users' scientific knowledge and accelerating the transformation efficiency of official nodes. This study can help provide a better understanding of the process of online rumor spreading and reversing, as well as offering ceritain guidance and countermeasures for online rumor control during public health emergencies.

show abstract

“…A framework for social media analysis has been built by existing studies, which includes four main steps: topic determination, data collection, data preprocessing and data analysis [29]. The first step in social media analysis is to determine the topic.…”

Section: Framework For Social Media Analysismentioning

confidence: 99%

A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis

et al. 2022

Self Cite

View full text Add to dashboard Cite

In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.

show abstract

Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China

Cited by 29 publications

References 41 publications

Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific

Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific

A rumor reversal model of online health information during the Covid-19 epidemic

A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis

Contact Info

Product

Resources

About