A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

Albuquerque, João Porto de; Herfort, Benjamin; Brenning, Alexander; Zipf, Alexander

doi:10.1080/13658816.2014.996567

Cited by 331 publications

(182 citation statements)

References 33 publications

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“…The steadily increasing stream of data from social media sources is relatively accessible and offers promising insights into the motivations and mechanisms of collective as well as individual human behavior. It has already been used to predict box office returns [7], elections [8] and flu trends [9], or even in disaster management and prediction [10]. At the same time, the wide-scale adoption and ubiquity of location aware technologies in networked mobile devices such as smartphones provide geography and especially geographic information science with additional opportunities since, as Gordon and de Souza e Silva [11] point out: "Mobile devices are the primary tools with which we access location."…”

Section: Introductionmentioning

confidence: 99%

Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production

Rzeszewski¹,

Beluch²

2017

IJGI

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Social media is a rich source of spatial data but it has also many flaws and well-known limitations, especially in regard to representation and representativeness, since very little is known about the demographics of the user population. At the same time, the use of locational services, is in fact, dependent on those characteristics. We address this gap in knowledge by exploring divides between Twitter users, based on the spatial and temporal distribution of the content they produce. We chose five cities and data from 2015 to represent different socio-spatial contexts. Users were classified according to spatial and non-spatial measures: home range estimation; standard distance; nearest neighbor index, and; proposed localness index. There are distinct groups of geosocial media producers, which suggests that such datasets cannot be treated as uniform representations. We found a positive correlation between spatial behavior and posting activity. It is suggested that there are universal patterns of behavior that are conditioned by software services-the example of Foucauldian "technologies of self". They can also represent the dominance of the most prolific users over the whole data stream. Results are discussed in the context of the importance and role of user location in social media.

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Section: Introductionmentioning

confidence: 99%

Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production

Rzeszewski¹,

Beluch²

2017

IJGI

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“…Geotagged social media data has therefore been explored to support the management of both those highly harmful ones (e.g., floods, earthquakes, fires, tsunamis, and typhoons), and those relatively less attended ones such as landslides. For example, De Albuquerque et al [20] investigated flood-related Tweets in Germany for the sake of both response and preventive monitoring. Zook et al [21] discussed how Twitter assisted the aftermath rescue of 2010 Haitian earthquake.…”

Section: Geotagged Social Media Data For Disaster Managementmentioning

confidence: 99%

Monitoring and Assessing Post-Disaster Tourism Recovery Using Geotagged Social Media Data

Yan

Eckle

Kuo

et al. 2017

IJGI

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Abstract:Tourism is one of the most economically important industries. It is, however, vulnerable to disaster events. Geotagged social media data, as one of the forms of volunteered geographic information (VGI), has been widely explored to support the prevention, preparation, and response phases of disaster management, while little effort has been put on the recovery phase. This study develops a scientific workflow and methods to monitor and assess post-disaster tourism recovery using geotagged Flickr photos, which involve a viewshed based data quality enhancement, a space-time bin based quantitative photo analysis, and a crowdsourcing based qualitative photo analysis. The developed workflow and methods have also been demonstrated in this paper through a case study conducted for the Philippines where a magnitude 7.2 earthquake (Bohol earthquake) and a super typhoon (Haiyan) occurred successively in October and November 2013. In the case study, we discovered spatiotemporal knowledge about the post-disaster tourism recovery, including the recovery statuses and trends, and the photos visually showing unfixed damages. The findings contribute to a better tourism rehabilitation of the study area.

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“…Due to the high degree of freedom and openness of Twitter, massive amounts of useless information that is unrelated to significant events is broadcast that simply reports common interactions among friends. Moreover, Twitter can be considered as a large black box that contains numerous topics reflecting various events from different domains, e.g., disasters [3], crimes [4], traffic [5], and epidemics [6]. Ways to extract hidden, unknown and significant events from the huge mass of Twitter data has thus become a research hotspot in computer science [7,8], human science [9,10] and GIS [11][12][13][14] in recent years.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter

Shi

Deng

Yang

et al. 2016

IJGI

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Abstract:In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twitter events and do not consider the spatio-temporal evolving process of Twitter events. In this paper, a framework is proposed to discover evolving domain related spatio-temporal patterns from Twitter data. Given a target domain, a dynamic query expansion is employed to extract related tweets to form spatio-temporal Twitter events. The new spatial clustering approach proposed here is based on the use of multi-level constrained Delaunay triangulation to capture the spatial distribution patterns of Twitter events. An additional spatio-temporal clustering process is then performed to reveal spatio-temporal clusters and outliers that are evolving into spatial distribution patterns. Extensive experiments on Twitter datasets related to an outbreak of civil unrest in Mexico demonstrate the effectiveness and practicability of the new method. The proposed method will be helpful to accurately predict the spatio-temporal evolution process of Twitter events, which belongs to a deeper geographical analysis of spatio-temporal Big Data.

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A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

Cited by 331 publications

References 33 publications

Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production

Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production

Monitoring and Assessing Post-Disaster Tourism Recovery Using Geotagged Social Media Data

A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter

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