OMG Earthquake! Can Twitter Improve Earthquake Response?

Earle, Paul S.; Guy, Michelle R.; Buckmaster, R. A.; Ostrum, Chris; Horvath, Scott; Vaughan, Amy

doi:10.1785/gssrl.81.2.246

Cited by 172 publications

(99 citation statements)

References 5 publications

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“…A team lead by Patrick Meijer collected tweets of observers and placed them on a map using the Ushahidi platform, assisting rescue operations [9]. Since then, social monitoring tools have been used regularly during or after disasters, including wildfires [19], earthquakes [20], floods [21], winter storms [22], heavy snowfall [23], and typhoons [24]. Several platforms have been designed to support these efforts, such as Twitter Alerts and GeoFeedia.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Jongman

Wagemaker²,

Revilla-Romero

et al. 2015

IJGI

126

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Humanitarian organizations have a crucial role in response and relief efforts after floods. The effectiveness of disaster response is contingent on accurate and timely information regarding the location, timing and impacts of the event. Here we show how two near-real-time data sources, satellite observations of water coverage and flood-related social media activity from Twitter, can be used to support rapid disaster response, using case-studies in the Philippines and Pakistan. For these countries we analyze information from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity analysis. The results demonstrate that these sources of near-real-time information can be used to gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods. In terms of location, we produce daily impact maps based on both satellite information and social media, which can dynamically and rapidly outline the affected area during a disaster. In terms of timing, the results show that GFDS and/or Twitter signals flagging ongoing or upcoming flooding are regularly available one to several days before the event was reported to humanitarian organizations. 2247In terms of event understanding, we show that both GFDS and social media can be used to detect and understand unexpected or controversial flood events, for example due to the sudden opening of hydropower dams or the breaching of flood protection. The performance of the GFDS and Twitter data for early detection and location mapping is mixed, depending on specific hydrological circumstances (GFDS) and social media penetration (Twitter). Further research is needed to improve the interpretation of the GFDS signal in different situations, and to improve the pre-processing of social media data for operational use.

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Section: Introductionmentioning

confidence: 99%

“…Earle et al (2010) show that Twitter can also be used for the rapid mapping of disasters. Their results demonstrate that Twitter messages can be used to delineate earthquake-affected areas within several minutes, before official earthquake observations are available.…”

Section: Introductionmentioning

confidence: 99%

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Jongman

Wagemaker²,

Revilla-Romero

et al. 2015

IJGI

126

View full text Add to dashboard Cite

show abstract

“…The other type assumes some correlations of the data on the social media and other data sources, and use statistical models, such a linear model, among them. For example, after an earthquake occurs, many people tweets about it [16], [17], when people are infected influenza, many people search about influenza [6], and popular movies are likely to be tweeted more frequently [11]. Basically, the later type just uses data on the social media, but predict trends for other objects, such as movies or patient of influenza.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, typical methods to predict trends for data on the social media try to grasp trends by analyzing occurrences of words or phrases in given data [10]- [16]. For example, [11] uses mentions to movies in tweets, [16], [17] mentions about earthquakes, and [3], [6], [7] query words used at search engines.…”

Section: Introductionmentioning

confidence: 99%

Predicting Latent Trends of Labels in the Social Media Using Infectious Capacity

Sonoda¹,

Ikeda²

2015

IJFCC

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Abstract-This paper is devoted to predicting trends on the social media. Typical methods in the literature are based on temporal changes in usage of words or phrases on the media, and try to find a rapid increase, called a burst, of them. Therefore, these methods can be applied only after a burst is emerging. In this paper, we propose an index, called the infectious capacity, to detect potential trends on the social media before they would emerge. To achieve this, we focus on labels and items, and predict trends of a label, instead of those of a target object, such as contents of a social media, where an item is a concept represented by an object and a label categories items. On a photo sharing service, for example, a photo is an object, a tag is a label, and concepts represented by a photo are items for the photo. Using labels and items, the infectious capacity for a label is defined as the ratio of the variety of items with the label to the number of occurrences of the label in given data. That is, the larger value an infectious capacity of a label is, more infectious the label is. Our experiments on real data showed that the infectious capacities for most labels are substantially constant over time. This result means that we can forecast the variety per usage for a label just after the label is used. Moreover, we found that infectious capacities for popular labels have similar values. Combined with the first result, we are able to predict latent trends before labels become popular. In fact, this is also supported by experiments on tweets, where we were able to find potentially popular hashtags, regarding hashtags as labels, before they become popular. As far as the authors know, this is the first result of future trend prediction on the social media.

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“…Indeed, previous studies have shown that Twitter users, who post short updates (of a maximum 140 characters in length) known as "tweets," will often share details about the conditions around them. This is especially true for largescale events such as earthquakes (Earle et al 2010;Crooks et al 2013), influenza outbreaks (Culotta 2010;Lampos et al 2010), and service outages (Motoyama et al 2010). Case et al (2015a) showed that Twitter can also be a useful source of data for studying the aurora by comparing the number of tweets relating to an aurora with auroral activity (or, more specifically, to common auroral activity indices).…”

Section: Introductionmentioning

confidence: 99%

Determining the Accuracy of Crowdsourced Tweet Verification for Auroral Research

Case¹,

MacDonald²,

McCloat³

et al. 2016

CSTP

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The Aurorasaurus project harnesses volunteer crowdsourcing to identify sightings of an aurora (the "northern/southern lights") posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter with the caveat that there is a large background level of non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by using volunteers to sift through a pre-filtered list of geolocated tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geolocating tweets, is described and its accuracy (which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings, so care must be taken when relying on crowdsourced identification.

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OMG Earthquake! Can Twitter Improve Earthquake Response?

Cited by 172 publications

References 5 publications

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Predicting Latent Trends of Labels in the Social Media Using Infectious Capacity

Determining the Accuracy of Crowdsourced Tweet Verification for Auroral Research

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