Using Analytics and Social Media for Monitoring and Mitigation of Social Disasters

Teodorescu, Horia-Nicolai

doi:10.1016/j.proeng.2015.06.088

Cited by 44 publications

(12 citation statements)

References 6 publications

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“…Takahashi et al [76] and Teodorescu [77] were researching the frequency and content of posts on SNS during natural disasters. During Typhoon Haiyan the highest number of posts on Twitter contained information from other media, for example, from the TV.…”

Section: Response Phasementioning

confidence: 99%

“…However, a large number of posts appeared with the expression of emotions or support to all those who are affected by typhoon, as well as the publication of volunteer activities, for the collection of food and other means of assistance [76]. Based on the analysis of tweets containing the words "blizzard" and "snow" during the storm, the results showed about 20,000 messages per day, during a snowstorm in North America in 2015 [77].…”

Section: Response Phasementioning

confidence: 99%

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Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide

et al. 2019

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Social networking sites (SNS) became an indispensable part of people’s everyday life, but also a powerful tool of communication during urgent situations, such as during natural disasters. This is evidenced by a large number of research papers showing the use of SNS in difficult circumstances. Some of the ways of using are the dissemination of information about missing persons, warning on further possible consequences, safety checks during natural disasters, communication about places where the population can find help or a safe refuge. At the same time, SNS could increase awareness among the population about natural hazards. Unfortunately, most parts of the world have at least once been hit by a major natural disaster. People who manage such events have a big task in front of them, as they need to exploit the potential of SNS, but also to reduce the negative side, such as spreading inaccurate information in difficult moments. The paper presents ways of using SNS, and the positive and negative effects of these, before, during and after natural disasters.

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Section: Response Phasementioning

confidence: 99%

Section: Response Phasementioning

confidence: 99%

Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide

et al. 2019

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“…Furthermore, the bag-of-words (BoW) approach, the feature abstraction, the feature selection and the Latent Dirichlet Allocation (LDA) were applied into feature representation as inputs for learning a classifier. Besides, various techniques of machine learning have been used for analysis of social media data to detect and track emergencies [112,113,128].…”

Section: Event Detection and Trackingmentioning

confidence: 99%

Analytics and Evolving Landscape of Machine Learning for Emergency Response

Hong

Akerkar

2019

Learning and Analytics in Intelligent Systems

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The advances in information technology have had a profound impact on emergency management by making unprecedented volumes of data available to the decision makers. This has resulted in new challenges related to the effective management of large volumes of data. In this regard, the role of machine learning in mass emergency and humanitarian crises is constantly evolving and gaining traction. As a branch of artificial intelligence, machine learning technologies have the huge advantages of self-learning, self-organization, and self-adaptation, along with simpleness, generality and robustness. Although these technologies do not perfectly solve issues in emergency management. They have greatly improved the capability and effectiveness of emergency management. In this paper, we review the use of machine learning techniques to support the decision-making processes for the emergency management and discuss their challenges. Additionally, we discuss the challenges and opportunities of the machine learning approaches and intelligent data analysis to distinct phases of emergency management. Based on the literature review, we observe a trend to move from narrow in scope, problem-specific applications of machine learning to solutions that address a wider spectrum of problems, such as situational awareness and real-time threat assessment using diverse streams of data. This chapter also focuses on crowd-sourcing approaches with machine learning to achieve better understanding and decision support during an emergency. Keywords emergency management • crisis analytics • data analysis • data mining • machine learning • decision making • situational awareness • real-time assessment • deep learning • data streams

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“…Advanced technologies can benefit from the development of the new means and methods of data and information transmission, including the Internet, social networks and social media [ 1 ]. Various types of research analyze the benefit of social media in monitoring the defending reaction against the disasters and for making accurate strategies in real-time by responding to the urgent population needs [ 2 – 4 ]. They focused on the use of social media in relation with different kinds of disasters such as the earthquake [ 5 – 7 ], tsunami [ 8 ], typhoons [ 9 ], storms [ 10 ], flood [ 11 ] and the health risks such as epidemic virus spreading [ 12 ], prediction of the contagious population behaviour and accurate detection and identification of professionally unreported drug side effects using widely available public data (open data) [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Taieb

Aouicha

2021

Appl Intell

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Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the healthcare practices and curing domain. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited Twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modeling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: first period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken according to the various languages under discussion representing several countries.

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Using Analytics and Social Media for Monitoring and Mitigation of Social Disasters

Cited by 44 publications

References 6 publications

Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide

Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide

Analytics and Evolving Landscape of Machine Learning for Emergency Response

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

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