Microblogging after a major disaster in China

Yan, Qiben; Huang, Chen; Zhang, Pengyi; Zhang, Jun

doi:10.1145/1958824.1958830

Cited by 262 publications

(36 citation statements)

References 14 publications

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“…ICTs provide both functional and emotional support. In China, people used Microblogging services and online forums to disseminate information, express opinions, offer emotional support and coordinate resident-to-resident assistance in response to earthquakes [50,51]. In the aftermath of the April 16, 2007 Virginia Tech shooting, the public collectively discovered the names of victims using online sites such as Facebook [67].…”

Section: Icts and Crisis Responsementioning

confidence: 99%

Managing Uncertainty

Gui

Kou

Pine

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Recently, diseases like H1N1 influenza, Ebola, and Zika virus have created severe crises, requiring public resources and personal behavior adaptation. Crisis Informatics literature examines interconnections of people, organizations, and IT during crisis events. However, how people use technology to cope with disease crises (outbreaks, epidemics, and pandemics) remains understudied. We investigate how individuals used social media in response to the outbreak of Zika, focusing on travel-related decisions. We found that extreme uncertainty and ambiguity characterized the Zika virus crisis. To cope, people turned to social media for information gathering and social learning geared towards personal risk assessment and modifying decisions when dealing with partial and conflicting information about Zika. In particular, individuals sought local information and used socially informed logical reasoning to deduce the risk at a specific locale. We conclude with implications for designing information systems to support individual risk assessment and decision-making when faced with uncertainty and ambiguity during public health crises.

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Section: Icts and Crisis Responsementioning

confidence: 99%

Managing Uncertainty

Gui

Kou

Pine

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…Generally speaking, there are three ways to use social media [11]. One is to regard social media as a huge sensor through which we can collect a lot of information [12] about typhoon disasters. The difficulty lies in that the sensor is too sensitive.…”

Section: Analysis Of Existing Studiesmentioning

confidence: 99%

Extracting Typhoon Disaster Information from VGI Based on Machine Learning

Zhao

Chin

2019

JMSE

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The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.

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“…Several studies have investigated information flow and user behavior on microblogging platforms during common emergency and crisis situations (e.g., Hua et al, ; Mendoza, Poblete, & Castillo, ; Qu, Huang, Zhang, & Zhang, ; Starbird, Maddock, Orand, Achterman, & Mason, ; Starbird & Palen, 2010, 2011; Vieweg, Hughes, Starbird, & Palen, ). The studies point to common reporting behavior during events, low variance in vocabulary, the importance of retweets, and also message attributes such as their increased frequency and contraction in length during major events.…”

Section: Related Workmentioning

confidence: 99%

Event Prediction With Learning Algorithms—A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data

Boecking¹,

Hall²,

Schneider³

2015

Policy & Internet

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We aim to predict activities of political nature influencing or reflecting societal‐scale behavior and beliefs by applying learning algorithms to Twitter data. This study focuses on capturing domestic events in Egypt from November 2009 to November 2013. To this extent we study underlying communication patterns by evaluating content and metadata of 1.3 million tweets through computationally supported classification, without targeting specific keywords or users from the Twitter stream. Support Vector Machine (SVM) and Support Distribution Machine (SDM) classification algorithms are applied to detect and predict societal‐scale unrest. Latent Dirichlet Allocation (LDA) is used to create content‐based input patterns for the SVM while the SDM is used to classify sets of features created from meta‐data. The experiments reveal that user centric approaches based on meta‐data outperform methods employing content‐based input despite the use of well established natural language processing algorithms. The results show that distributions over user centric meta information provide an important signal when detecting and predicting events. Applying this approach can assist policymakers and stakeholders in their efforts toward proactive community management.

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Microblogging after a major disaster in China

Cited by 262 publications

References 14 publications

Managing Uncertainty

Managing Uncertainty

Extracting Typhoon Disaster Information from VGI Based on Machine Learning

Event Prediction With Learning Algorithms—A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data

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