Forecasting People’s Needs in Hurricane Events from Social Network

Nguyen, Long; Yang, Zhou; Jia, Li; Pan, Zhenhe; Cao, Guofeng

doi:10.1109/tbdata.2019.2941887

Cited by 32 publications

(16 citation statements)

References 28 publications

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“…To capture the real-time dynamics of input, Addict Free utilizes a LSTM model for relapse time series prediction due to its wellhandling ability of long and short term time dependency. The model is also utilized in other types of prediction as in [4] and [1][2][3]. Figure 5 shows the basic structure of LSTM.…”

Section: Methods 41 Relapse Predictionmentioning

confidence: 99%

Addict Free - A Smart and Connected Relapse Intervention Mobile App

Yang

Reddy

Kesidi

2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

Self Cite

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It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those factors. In this paper, we introduce a mobile app called "Addict Free", which records user profiles, tracks relapse history and summarizes recovering statistics to help users better understand their recovering situations. Also, this app builds a relapse recovering community, which allows users to ask for advice and encouragement, and share relapse prevention experience. Moreover, machine learning algorithms that ingest spatial and temporal factors are utilized to predict relapse, based on which helpful addiction diversion activities are recommended by a recovering recommendation algorithm. By interacting with users, this app targets at providing smart suggestions that aim to stop relapse, especially for alcohol and tobacco addiction users. CCS CONCEPTS• Applied computing → Health care information systems.

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Section: Methods 41 Relapse Predictionmentioning

confidence: 99%

Addict Free - A Smart and Connected Relapse Intervention Mobile App

Yang

Reddy

Kesidi

2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

Self Cite

View full text Add to dashboard Cite

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“…Such interactions have attracted a significant number of users, which is increasing steadily, and resulted in the appearance of many different types of social networks [92], [93]. In fact, social networking is playing a fundamental role in modern society, even modifying the way most daily human activities are conducted by leveraging the conducted relationships through the collection of user profiles that join and establish social links with each other [94]; for instance, information sharing and specific shared resources search, events organization, job searching or commercial advertising, among many others [95]. As a result, the number of users registered in social networks increases exponentially every day.…”

Section: A the Rise Of Social Networkmentioning

confidence: 99%

Analysis of Remotely Sensed Images Through Social Media

Redondo

Haut

Paoletti

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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During the past decades, the volume of big data available in remote sensing (RS) applications has grown significantly. In addition, a number of applications related to monitoring human activity are being developed based on this kind of data. This has considerably increased the demand for RS processing methods. In this sense, the scientific community is facing the challenge of how to maximize the potential of the data that are produced in a fast and efficient way. In particular, the provision of processing algorithms that can be developed in an easy way is a fundamental problem for the RS community, due to the large volume of data offered by different portals and agencies, and the need for algorithms developed on different platforms and using a variety of programming frameworks. To address these challenges, this paper takes advantage of social media tools to bring images and algorithms closer to users. In particular, a new system based on the Telegram messaging application Bot (called @ThuleRSbot) has been developed to provide a wide variety of RS image processing methods to users under the same interface. The system has been developed using the Python language and the Telegram Bot application program interface (API). The most remarkable characteristics of the system are: 1) a completely open architecture that facilitates the incorporation of new algorithms without effort, and 2) an easy way to automatically gather satellite images from the Sentinel Hub platform. Code available on: https://github.com/mhaut/ThuleRSbot.

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“…Identifying posts related to different types of disruptions and impacts is a fine-grained classification task, which is much more challenging to achieve than the existing classification approaches, which classify posts as to disaster-relatedness. The mainstream of fine-grained approaches is based on supervised learning from hundreds of thousands of labeled posts (Burel, Saif, & Alani, 2017;Nguyen, Yang, Li, Cao, & Jin, 2018;Zhang et al, 2019). Due to the uniqueness of each disaster setting and community, achieving a comprehensive training dataset is not feasible.…”

Section: Labeling Of Social Media Posts According To the Taxonomymentioning

confidence: 99%

Semiautomated social media analytics for sensing societal impacts due to community disruptions during disasters

Zhang

Yao

Yang

et al. 2020

Computer aided Civil Eng

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Understanding the societal impacts caused by community disruptions (e.g., power outages and road closures), particularly during the response stage, with timeliness and sufficient detail is an underexplored, yet important, consideration. It is critical for effective decision-making and coordination in disaster response and relief activities as well as post-disaster virtual reconnaissance activities. This study proposes a semiautomated social media analytics approach for social sensing of Disaster Impacts and Societal Considerations (SocialDISC). This approach addresses two limitations of existing social media analytics approaches: lacking adaptability to the need of different analyzers or different disasters and missing the information related to subjective feelings, emotions, and opinions of the people. SocialDISC labels and clusters social media posts in each disruption category to facilitate scanning by analyzers. Analyzers, in this paper, are persons who acquire social impact information from social media data (e.g., infrastructure management personnel, volunteers, researchers from academia, and some residents impacted by the disaster). Furthermore, SocialDISC enables analyzers to quickly parse topics and emotion signals of each subevent to assess the societal impacts caused by disruption events. To demonstrate the performance of SocialDISC, the authors proposed a case study based on Hurricane Harvey, one of the costliest disasters in U.S. history, and analyzed the disruptions and corresponding societal impacts in different aspects. The analysis result shows that Houstonians suffered greatly from flooded houses, lack of access to food and water, and power outages. SocialDISC can foster an understanding of the relationship between disruptions of infrastructures and societal impacts, expectations of the public when facing disasters, and infrastructure interdependency and cascading failures. SocialD-ISC's provision of timely information about the societal impacts of people may help disaster response decision-making. 1 INTRODUCTION Disaster-induced community disruptions include physical infrastructure and social disruptions, such as hazards, lack of supplies, business interruption losses, and evacuation orders

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Forecasting People’s Needs in Hurricane Events from Social Network

Cited by 32 publications

References 28 publications

Addict Free - A Smart and Connected Relapse Intervention Mobile App

Addict Free - A Smart and Connected Relapse Intervention Mobile App

Analysis of Remotely Sensed Images Through Social Media

Semiautomated social media analytics for sensing societal impacts due to community disruptions during disasters

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