Dataset Information

A global database of historic and real-time flood events based on social media.

ABSTRACT: Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10,000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE were detected. All results are publicly available on www.globalfloodmonitor.org.

SUBMITTER: de Bruijn JA

PROVIDER: S-EPMC6901592 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A global database of historic and real-time flood events based on social media.

de Bruijn Jens A JA de Moel Hans H Jongman Brenden B de Ruiter Marleen C MC Wagemaker Jurjen J Aerts Jeroen C J H JCJH

Scientific data 20191209 1

Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 ...[more]

PMID: 31819066

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Project description:Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k?=?1?=?0.7618?±?0.0018 (chance 0.397?±?0.004, mean?±stdev?). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org . We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

Project description:BackgroundIn April 2020, as cases of the novel COVID-19 spread across the globe, MTV Staying Alive Foundation created the educational entertainment miniseries MTV Shuga: Alone Together. In 70 short episodes released daily on YouTube, Alone Together aimed to disseminate timely and accurate information to increase young people's knowledge, motivation, and actions to prevent COVID-19.ObjectiveWe sought to identify Alone Together viewer's perspectives on the global COVID-19 pandemic and national lockdowns by examining the words, conversations, experiences, and emotions expressed on social media in response to the Alone Together episodes. We also assessed how viewers used the series and its online community as a source of support during the global pandemic.MethodsA total of 3982 comments and 70 live chat conversations were extracted from YouTube between April and October 2020 and analyzed through a data-led inductive thematic approach. Aggregated demographic and geographical data were collected using YouTube Analytics.ResultsThe miniseries had a global reach across 5 continents, with a total of 7.7 million views across MTV Shuga platforms. The series had over 1 million views over 70 episodes on YouTube and an average of 5683 unique viewers per episode on YouTube. The dominant audience was adults under the age of 35 years and women. Across diverse countries such as Nigeria, Ghana, the United States, and the UK, viewers believed that COVID-19 was serious and expressed that it was socially responsible to follow public health measures. Storylines of the series about the impact of self-isolation on mental health, exposure to violence in lockdowns, and restricted employment opportunities due to the pandemic resonated with young viewers. Tuning in to the miniseries provided viewers with reliable information, entertainment, and an online community during an isolating, confusing, and worrying time.ConclusionsDuring the first wave of COVID-19, viewers from at least 53 countries connected on social media via the MTV miniseries. The analysis showed how digitally connected people under the age of 35 years, predominantly women, felt compelled to follow COVID-19 safety measures despite the pandemic's impact on their social, educational, and financial needs. Viewers used social media to reach out to fellow viewers for advice, solace, support, and resources. Organizations, governments, and individuals have been forced to innovate during the pandemic to ensure people can access services safely and remotely. This analysis showed that women under 35 years of age were especially receptive to receiving support from online communities and media services. Peer influence and support online can be a powerful public health tool as people have a great capacity to influence each other and shape norms around public health. However, online services are not accessible to everyone, and COVID-19 has increased disparities between digitally connected and unconnected younger adults.

Dataset Information

A global database of historic and real-time flood events based on social media.

Publications

A global database of historic and real-time flood events based on social media.

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