Event classification and location prediction from tweets during disasters

Abstract

Social media is a platform to express one's view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

Ranking online consumer reviews

“…They extracted 20 features and found that the product type and textual characteristics are important determinants for review helpfulness when predicting reader's perception. Liu et al (2007) addressed the problem of detecting low-quality reviews using a classificationbased approach (Singh et al, 2017b). Three aspects of product reviews were explored, specifically, informativeness, subjectiveness, and readability.…”

Location reference identification from tweets during emergencies: A deep learning approach

“…We assume that there is already a system that filters tweets based on their relatedness to a particular event. Several works have been reported regarding this (Chowdhury et al, 2013;Imran et al, 2014a;Nguyen et al, 2017a;Olteanu et al, 2014;Singh et al, 2017). Once the tweets are found to be related to the event, our model finds the location referring words in that tweet.…”

“…Several examples were seen when the news was first reported on Twitter, such as an airplane crash over the Hudson River in New York in the year 2009 (Sakaki et al, 2013), the death of former British Prime Minister Margaret Thatcher in April 2013 1 , and the explosions at the Boston Marathon 2013 1 . In recent years, Twitter has been used extensively in the course of natural and human-made disasters such as earthquakes, floods, fire, terrorist attacks, civil unrest, and so on (Alexander, 2014;Landwehr et al, 2016;Laylavi et al, 2017Laylavi et al, , 2016Luna & Pennock, 2018;Mejri et al, 2017;Mendoza et al, 2010;Sakaki et al, 2013;Singh et al, 2017;Yuan & Liu, 2018). The government and non-government agencies use Twitter in case of crisis so that different rescue operations can leap into action, disseminate information to the wider audience, and recognize floor reality (Imran et al, 2014a(Imran et al, , 2015Landwehr et al, 2016;Laylavi et al, 2017Laylavi et al, , 2016Rossi et al, 2018;Sakaki et al, 2013;Zhou et al, 2017).…”

Knowledge model for emergency response based on contingency planning system of China

“…Much work has been done in the knowledge engineering (Altay & Pal, 2014;Singh, Dwivedi, Rana, Kumar, & Kapoor, 2017) for emergency management (Mendis, Karunananda, Samaratunga, & Ratnayake, 2007a;Yates & Paquette, 2011): ontology (Kollarits, Wergles, & Siegel, 2009), description logics (Grathwohl & de Beuvron, 1999), TTL (Hoogendoorn, Jonker, Popova, & Sharpanskykh, 2005), narrative networks (Constantinides & Barrett, 2012), fuzzy logic (Mendis, Karunananda, Samaratunga, & Ratnayake, 2007b), object-oriented constraint networks (Smirnov, Levashova, & Shilov, 2015), linguistic model (Zhang, Wang, & Zhao, 2018), space modeling (Xie, Li, Wei, Jiang, & Xie, 2016) and so on. Commonly, emergency management Dwivedi, Shareef, & Mukerji, 2017;Sinha, Kumar, Rana, Islam, & Dwivedi, 2017) includes four phases: mitigation, preparedness, response and recovery.…”