Identifying Damage-Related Social Media Data during Hurricane Matthew: A Machine Learning Approach

Yuan, Faxi; Li, Rui

doi:10.1061/9780784482421.027

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Some studies (Kotsiantis, Zaharakis, & Pintelas, 2007) adopted supervised machine learning based classification approaches to resolve this limitation. These studies (Cresci, Cimino, Dell'Orletta, & Tesconi, 2015; Yuan & Liu, 2019) adopted support vector machine (SVM), naïve Bayes, decision tree (DT) classification algorithms to analyze damage‐related social media posts. However, these studies considered damage identification as a binary (damage related or not) classification problem, which may include posts that are not reporting an actual damage/disruption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, to get actionable information, it is important to identify whether a post indicates an actual disruption or simply expresses user views or opinions about a disruption. Recent studies have mainly focused on identifying whether a particular social media post is damage related or not (Yuan & Liu, 2018, 2019). However, since infrastructure systems are more interconnected, co‐occurrences of disruptions in multiple infrastructures are more likely.…”

Section: Introductionmentioning

confidence: 99%

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A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

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Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the cooccurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption-whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo-parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption-related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co-occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations. 1 INTRODUCTION Cities and communities all over the world largely depend on critical infrastructure systems/services such as electrical power, water distribution, communication services, and transportation networks. The growing interconnectedness and © 2020 Computer-Aided Civil and Infrastructure Engineering interdependency among these systems have changed the organizational and operational factors and increased the vulnerability in the face of unwanted disruptions. These systems provide critical services to a large population, and thus when disrupted they affect our quality of life, local and regional economy, and the overall community wellbeing. The need to

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

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“…As a complement to qualitative survey data, researchers have relied upon social media platforms to quantify data related to community resilience. Social media data have been applied to capturing societal disruptions [35], conducting rapid damage assessment [40][41][42], and sensing the dynamic situation of infrastructure services [43]. Social media data, however, can overlook certain demographic groups based on user preferences [44].…”

Section: Introductionmentioning

confidence: 99%

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

Podesta

Coleman

Esmalian

et al. 2021

J. R. Soc. Interface.

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This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.

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“…Hence, in addition to survey data, researchers have utilized social media platforms in community resilience assessment studies. Social media data has been applied to capturing societal disruptions (34), conducting rapid damage assessment (39,40,41), and sensing the dynamic situation of infrastructure services (42). Social media data, however, has limitations related to representativeness and can overlook certain demographic groups (43).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Community Resilience Based on Fluctuations in Visits to Point-of-Interest from Digital Trace Data

Podesta,

Coleman,

Esmalian

et al. 2020

Preprint

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The objective of this study is to quantify community resilience based on fluctuations in the visits to various Point-of-Interest (POIs) locations during different stages of a crises. Visits to POIs is an important indicator of population activities and captures the combined effects of perturbations in people lifestyles, the built environment conditions, and the status of businesses. The study utilized digital trace data related to unique visits to POIs in the context of the 2017 Hurricane Harvey in Houston (Texas, USA) to examine the spatial patterns of impacts and total recovery efforts and utilized these measures in quantifying community resilience. The results showed that certain POI categories such as building materials and supplies dealers and grocery stores were the most resilient elements of the community compared to the other POI categories. On the other hand, categories such as medical facilities and entertainment were found to have lower resilience values. This result suggests that these categories were either not essential for community recovery or that the community was not able to access these services at normal levels immediately after the hurricane. In addition, the spatial analyses revealed that many areas in the community with lower levels of resilience (high systematic impact and long total recovery effort) experienced extensive flooding. However, some areas with low resilience were not flooded extensively, suggesting that spatial reach of the impacts goes beyond flooded areas. These results demonstrate the importance and value of the approach proposed in this study for quantifying and analysing community resilience patterns using digital trace/location intelligence data related to population activities. While this study focused on the Houston area and only analysed one natural hazard, the same approach can be applied to other communities and disaster contexts. By applying this approach, emergency managers and public officials can efficiently monitor the patterns of disaster impacts and recovery across different spatial areas and POI categories and also identify POI categories and areas of their community that need to be prioritized for resource allocation.

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Identifying Damage-Related Social Media Data during Hurricane Matthew: A Machine Learning Approach

Cited by 8 publications

References 14 publications

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

Quantifying Community Resilience Based on Fluctuations in Visits to Point-of-Interest from Digital Trace Data

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