In 2013, Twitter launched the Twitter Alerts service to help agencies share information during crisis or emergency events (Twitter Inc. 2015 Ng and Lean 2012 Takahashi, Tandoc, and Carmichael 2015). In fact, Twitter is considered crucial for risk communication in addition to conventional technologies, such as mass media, TV, radio, etc. The ubiquity of computing and IoT devices has enabled individuals and organizations to use social media and social network sites to obtain and share information, coordinate disaster relief efforts, seek assistance during disasters (Estellés-Arolas and González-Ladrón-De-Guevara 2012). Effective risk communication, however, depends on dissemination of timely, relevant, complete, and reliable information to enable first responders, local public and other stakeholders undertake mitigation actions (Horita et al. Risk communication is a crucial component of emergency management, which focuses on disseminating information about an impending disaster to citizens to help them take timely preparatory actions to reduce impacts (Covello, McCallum, and Pavlova 1989 Hughes et al. Hurricanes Harvey, Irma, and Maria that impacted the US Atlantic Coast in 2017 provide an insight into the severity of climate related extreme events.Īlthough the occurrence of these events could not be prevented, their impacts on society and physical environments could be reduced through emergency management and mitigation activities. Due to continued population growth and rapid urbanization, the natural and built environments of coastal communities are at elevated risk to both coastal and riverine flooding events (Karl 2009 Karl et al. Since 1970s, there has been a worldwide increase in severity and frequency of hydro-meteorological events (i.e. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.

By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. Primarily, tweets containing information about the flooding events and its impacts were analysed. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. In this study, six different computational (i.e. Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate.

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