Crowdsourcing triggers rapid, reliable earthquake locations

Steed, Robert; Fuenzalida, A.; Bossu, Rémy; Bondár, István; Heinloo, Andres; Dupont, Aurélien; Saul, Joachim; Strollo, Angelo

doi:10.1126/sciadv.aau9824

Cited by 37 publications

(28 citation statements)

References 24 publications

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“…Geoscientists can also disseminate the information to people directly affected by the disaster and engage in discussion with them (e.g. Stewart et al, 2018). A rapid understanding is also crucial to evaluate the risk of cascading events (e.g.…”

Section: Introductionmentioning

confidence: 99%

Rapid collaborative knowledge building via Twitter after significant geohazard events

et al. 2020

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Abstract. Twitter is an established social media platform valued by scholars as an open way to disseminate scientific information and to publicly discuss research results. Scientific discussions on Twitter are viewed by the media, who can then pass on information to the wider public. Social media is used widely by geoscientists, but there is little documentation currently available regarding the benefits or limitations of this for the scientist or the public. Here, we use the example of two 2018 earthquake-related events that were widely commented on by geoscientists on Twitter: the Palu Mw 7.5 earthquake and related tsunami in Indonesia and the long-duration Mayotte island seismovolcanic crisis in the Indian Ocean. We built our study on a content and contextual analysis of selected Twitter threads about the geophysical characteristics of these events. From the analysis of these two examples, we show that Twitter promotes a very rapid building of knowledge in the minutes to hours and days following an event via an efficient exchange of information and active discussion between the scientists themselves and the public. We discuss the advantages and potential pitfalls of this relatively novel way of making scientific information accessible to scholarly peers and lay people. We argue that scientific discussion on Twitter breaks down the traditional “ivory tower” of academia, contributes to the growing trend towards open science, and may help people to understand how science is developed and, in turn, to better understand the risks related to natural/environmental hazards.

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Section: Introductionmentioning

confidence: 99%

Rapid collaborative knowledge building via Twitter after significant geohazard events

et al. 2020

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“…For around a decade now, earthquake scientists have begun to use information extracted from social media, websites, or app earthquake reporting, to automatically detect and locate earthquakes within tens of seconds of their occurrence time (Bossu et al, 2008(Bossu et al, , 2018Earle et al, 2010;Steed et al, 2019). Here, rather than relying on such a quantitative survey based on large-scale keywords or hashtags statistics, or website traffic analysis combined with geolocalisation, we build our study on the contextual analysis of qualitative content of selected Twitter conversational threads.…”

Section: -Studied Events and Methodologymentioning

confidence: 99%

Rapid collaborative knowledge building via Twitter after significant geohazard events

Lacassin¹,

Dévès²,

Hicks

et al. 2020

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<p>Twitter is an established social media platform valued by scholars as an open way to disseminate scientific information and to publicly discuss research results. Scientific discussions on Twitter are widely viewed by the media who can then pass on information to the public. Here, we take the example of two 2018 earthquake-related events which were widely commented on Twitter by geoscientists: the Palu Mw7.5 earthquake and tsunami in the Indonesian island of Sulawesi and the long-duration (more than one year) seismo-volcanic crisis Mayotte island in the Comoros archipelago between Africa and Madagascar. We build our analysis on a content and contextual analysis of selected Twitter threads about the geophysical characteristics of these events. Most authors of this paper have participated to these Twitter threads and related discussions, and regularly explain geohazard events via this social media. From the two selected examples, we show that Twitter promotes very rapid building of knowledge &#8211; in the minutes to hours and days following an event &#8211; via an efficient exchange of information and active discussion between the scientists themselves and with the public. Combining these results with our own experience of communicating geohazard science via Twitter, we discuss the advantages and potential pitfalls of this relatively novel way to make scientific information accessible to scholarly peers and to lay people. We argue that scientific discussion on Twitter breaks down the traditional &#8220;ivory towers&#8221; of academia. It participates to the growing trends towards open science, making science accessible to any non-academics or citizen scientists who can follow and participate in the discussion. This may help people to understand how science is developed, and, in the case of natural/environmental hazards, to better understand their risks.</p>

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“…Crowd-sourced geographical data (e.g., mobile check-in data, cellular signaling data, and taxi trajectory data) are rich in information, low cost, and abundant [14]. Such data have been widely used in sensing the geographical environment [15], recognizing urban structure and functional areas [16], planning urban development [17], assisting sustainable economic development [18], perceiving geographical events [19,20], and crowdmapping [21]. Therefore, social sensing based on crowd-sourced geographical data provides a practical approach to explore the spatial behavior of the public and reveal geographical features of the socioeconomy [22].…”

Section: Chinese Spring Festival Travelmentioning

confidence: 99%

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

et al. 2020

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Regional development differences are a universal problem in the economic development process of countries around the world. In recent decades, China has experienced rapid urban development since the implementation of the reform and opening-up policy. However, development differs across regions, triggering the migration of laborers from underdeveloped areas to developed areas. The interaction between regional development differences and Spring Festival has formed the world’s largest cyclical migration phenomenon, Spring Festival travel. Studying the migration pattern from public spatiotemporal behavior can contribute to understanding the differences in regional development. This paper proposes a geospatial network analytical framework to quantitatively characterize the imbalance of urban/regional development based on Spring Festival travel from the perspectives of complex network science and geospatial science. Firstly, the urban development difference is explored based on the intercity population flow difference ratio, PageRank algorithm, and attractiveness index. Secondly, the community detection method and rich-club coefficient are applied to further observe the spatial interactions between cities. Finally, the regional importance index and attractiveness index are used to reveal the regional development imbalance. The methods and findings can be used for urban planning, poverty alleviation, and population studies.

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Crowdsourcing triggers rapid, reliable earthquake locations

Cited by 37 publications

References 24 publications

Rapid collaborative knowledge building via Twitter after significant geohazard events

Rapid collaborative knowledge building via Twitter after significant geohazard events

Rapid collaborative knowledge building via Twitter after significant geohazard events

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

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