Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines

Takahashi, Bruno; Tandoc, Edson C.; Carmichael, Christine

doi:10.1016/j.chb.2015.04.020

Cited by 302 publications

(265 citation statements)

References 25 publications

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“…First of all, in contrast to the satellite-based inundation identification, flood-related tweets are not necessarily an indication of an ongoing flood at the same time and place as where the tweet was posted. Research has shown that less than 5% of the disaster related tweets during Typhoon Haiyan dealt with personal observations, whereas 40% reflected second-hand information (i.e., re-tweets, or tweets about news reports) [24]. Second, the heat maps are strongly biased towards urban areas, both those affected from floods and those outside of the flood plain.…”

Section: Rapid Flood Mappingmentioning

confidence: 99%

“…Whereas the metadata that comes with Twitter messages posted by a mobile device also include information on the geographical location of that device at the time of posting, this information is not used in this study. The first reason for this choice is the fact that the majority of tweets report second-hand information, i.e., they discuss events that do not happen at the location of the person publishing the tweet [24]. Second, even when a tweet refers to a first-hand observation, it is not obvious that this observation has happened at the same time and place of the sending of the tweet [30].…”

Section: Near-real-time Twitter Datamentioning

confidence: 99%

“…However, the biggest peak in the Twitter signal is recorded on the day of reporting (i.e., one day after the event occurred). This emphasizes that the majority of flood-related tweets are generally reporting second-hand information, by retweeting eye witness reports or news items [24].…”

Section: Rapid Flood Detectionmentioning

confidence: 99%

“…The main uses of social media in disaster situations have been improving situational awareness throughout the post-event response phase, two-way communication with affected people, psychological analysis [25], and relief coordination [24]. Earle et al (2010) show that Twitter can also be used for the rapid mapping of disasters.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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

Jongman

Wagemaker²,

Revilla-Romero

et al. 2015

IJGI

116

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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: Rapid Flood Mappingmentioning

confidence: 99%

Section: Near-real-time Twitter Datamentioning

confidence: 99%

Section: Rapid Flood Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jongman

Wagemaker²,

Revilla-Romero

et al. 2015

IJGI

116

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Big Data Approaches for coastal flood risk assessment and emergency response

Pollard

Spencer

Jude

2018

WIREs Climate Change

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Big Data Approaches (BDAs) refers to the combined use of historic datasets, incoming data streams, and the array of related technologies designed to shed new light on societal and environmental complexities through novel organizational, storage, and analytical capabilities. Despite widespread recognition of the commercial benefits of BDAs, application in the environmental domain is less well articulated. This represents a missed opportunity given that the dimensions used to characterize BDAs (volume, variety, velocity, and veracity) appear apt in describing the intractable challenges posed by global climate change. This paper employs coastal flood risk management as an illustrative case study to explore the potential applications in the environmental domain. Trends in global change including accelerating sea level rise, concentration of people and assets in low‐lying areas and deterioration of protective coastal ecosystems are expected to manifest locally as increased future flood risk. Two branches of coastal flood risk management are considered. First, coastal flood risk assessment, focusing on better characterization of hazard sources, facilitative pathways, and vulnerable receptors. Second, flood emergency response procedures, focusing on forecasting of flooding events, dissemination of warnings, and response monitoring. Critical commentary regarding technical, contextual, institutional, and behavioral barriers to the implementation of BDAs is offered throughout including a discussion of two fundamental difficulties associated with applying BDAs to coastal flood risk management: the role of BDAs in the broader flood system and the skill requirements for a generation of data scientists capable of implementing Big Data Approaches. This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice Climate, History, Society, Culture > Technological Aspects and Ideas

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