Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation

Abstract

In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported.

Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments

“…In contrast, Benitez et al (2018) classify tweets by disaster type, but do not perform geoparsing, relying on geotagged tweets instead. Similar other works classify, geoparse, and map tweets in realtime during various crises (Choi and Bae, 2015;Mao et al, 2018;Avvenuti et al, 2018;Anbalagan and Valliyammai, 2016;Hernandez-Suarez et al, 2019).…”

LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things

“…The ith sequence consists of W log messages from l iW+1 , l iW+2 , to l iW+W . Each log message in a log sequence can be mapped into a log event [47]. As a result, the log sequence can be treated as a list of log events.…”

Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study

“…There are various techniques for location extraction from formal or informal texts such as geographical information systems [9][10][11], natural language processing techniques especially named entity recognition [12][13][14][15], machine learning-based approaches [16][17][18][19][20], gazetteer-based approaches [21,22], and rule-based approaches [23]. These different techniques can be integrated and then used.…”