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

Abstract

Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios.The identification of geographic location is one of the challenging tasks as the location information fields, such as user location and place name of tweets are not reliable. The extraction of location information from tweet text is difficult as it contains a lot of non-standard English, grammatical errors, spelling mistakes, non-standard abbreviations, and so on. This research aims to extract location words used in the tweet using a Convolutional Neural Network (CNN) based model. We achieved the exact matching score of 0.929, Hamming loss of 0.002, and F 1 -score of 0.96 for the tweets related to the earthquake. Our model was able to extract even three-to four-word long location references which is also evident from the exact matching score of over 92%. The findings of this paper can help in early event localization, emergency situations, real-time road traffic management, localized advertisement, and in various location-based services.

Social Media Usage During Disasters: Exploring the Impact of Location and Distance on Online Engagement

“…11 But there is a significant difference in the social media usage pattern in terms of distance to the disaster site. 12,13 Citizens from affected areas turn to social media to share their experiences, seek help, and coordinate their response. Remote audiences participate in online interactions as spectators or volunteers who provide social support.…”

Crisis Informatics in the Context of Social Media Crisis Communication: Theoretical Models, Taxonomy, and Open Issues

“…Crisis informatics encompasses using social media in crisis management [33], [36]. The classification and evidence of social media intervention for crisis management includes social sensing [69], [70], which is further classified into mapping [71], [72], location identity [73]- [75] and geoweb [76]- [78]. Crowdsourcing [6], [55], [79]- [81] and digital volunteers [82]- [84] are the additional areas identified from the literature.…”

“…However, crisis informatics is a multidisciplinary area of research, and various issues and challenges were reported from diverse literature and several of these challenges remain unanswered. [27], [75] reported the issue of dataset inaccessibility and inefficiency. This is important to link present research work with previous work [160]- [162].…”

Time-Critical Geolocation for Social Good

“…Al-Olimat et al [1] proposed identifying the location names by traversing a tree of the tweet's n-grams to extract valid locations that exist in their pre-build region-specific gazetteer. Moreover, Hoang and Mothe [8] combined syntactic and semantic features to train traditional ML-based models whereas Kumar and Singh [13] trained a Convolutional Neural Network (CNN) model that learns the continuous representation of tweet text and then identifies the location mentions.…”