Real-Time Crisis Mapping of Natural Disasters Using Social Media

Middleton, Stuart E.; Middleton, Lee J; Modafferi, Stefano

doi:10.1109/mis.2013.126

Cited by 351 publications

(267 citation statements)

References 8 publications

(10 reference statements)

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“…Two case studies compare five-day tweet crisis maps to official post-event impact assessment from the US National Geospatial Agency (NGA), compiled from verified satellite and aerial imagery sources [31] Tweedr: Mining twitter to inform disaster response…”

Section: Title Descriptionmentioning

confidence: 99%

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Hernandez-Suarez¹,

Sánchez-Pérez²,

Toscano-Medina³

et al. 2018

Preprint

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Abstract:In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user's description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

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Section: Title Descriptionmentioning

confidence: 99%

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Hernandez-Suarez¹,

Sánchez-Pérez²,

Toscano-Medina³

et al. 2018

Preprint

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“…For online processing Tweets are geo-parsed [17] and locations matched to the offline database of coastal geographic information. Tweets are first tokenized into ngram tokens and then named entity matching performed.…”

Section: Hasc Region Shapefilesmentioning

confidence: 99%

“…New York hurricane Sandy 2012, Oklahoma tornado 2013, Philippines Tsunami 2012) keyword filtered Twitter Streaming API throughput of up to 20,000 tweets per hour [17]. Given the rapid adoption rate of social media around the world this throughput is only expected to get larger.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Decision Support System for Natural Crisis Management with TweetComP1

Middleton

Zielinski

Necmioğlu

et al. 2014

Decision Support Systems III - Impact of Decision Support Systems for Global Environments

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Abstract. This paper discusses the design of a social media crisis mapping platform for decision making in natural disasters where tweets are analysed to achieve situational awareness during earthquake and tsunami events. A qualitative end user evaluation is undertaken on our first prototype system to get feedback from practitioners working in the field of hazard detection and early warning. Participating in our evaluation is the Kandilli Observatory and Earthquake Research Institute (KOERI) and the Portuguese Institute for the Sea and Atmosphere (IPMA). We conclude that social media crisis mapping is seen as a valuable data source by control room engineers, with update rates of 10-60 seconds and false positive rates of 10-20% (general public incident reports) needed. Filtering crisis maps and statistical reports by social media platform and user type is desirable as different report sources have different credibility and response times.

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“…However, whether GPS information is included in tweets is controlled by the user, in their client settings. It was reported in a recent study (Middleton et al, 2014) that less than 1% of tweets have GPS information appended to them. LREs are expressed in natural languages in the tweet, and an analysis would make it possible to map the actual spatial entity.…”

Section: Introductionmentioning

confidence: 99%

Annotating Geographical Entities on Microblog Text

Matsuda¹,

Sasaki²,

Okazaki³

et al. 2015

Proceedings of the 9th Linguistic Annotation Workshop

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This paper presents a discussion of the problems surrounding the task of annotating geographical entities on microblogs and reports the preliminary results of our efforts to annotate Japanese microblog texts. Unlike prior work, we not only annotate geographical location entities but also facility entities, such as stations, restaurants, shopping stores, hospitals and schools. We discuss ways in which to build a gazetteer, the types of ambiguities that need to be considered, reasons why the annotator tends to disagree, and the problems that need to be solved to automate the task of annotating the geographical entities. All the annotation data and the annotation guidelines are publicly available for research purposes from our web site.

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Real-Time Crisis Mapping of Natural Disasters Using Social Media

Cited by 351 publications

References 8 publications

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Spatio-Temporal Decision Support System for Natural Crisis Management with TweetComP1

Annotating Geographical Entities on Microblog Text

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