A deep multi-modal neural network for informative Twitter content classification during emergencies
People start posting tweets containing texts, images, and videos as soon as a disaster hits
an area. The analysis of these disaster-related tweet texts, images, and videos can help …
an area. The analysis of these disaster-related tweet texts, images, and videos can help …
Location reference identification from tweets during emergencies: A deep learning approach
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 …
and relief operation in real time. The geographical location information of the event, as well …
GazPNE2: A general place name extractor for microblogs fusing gazetteers and pretrained transformer models
The concept of “human as sensors” defines a new sensing model, in which humans act as
sensors by contributing their observations, perceptions, and sensations. This is crucial for …
sensors by contributing their observations, perceptions, and sensations. This is crucial for …
Attention-based LSTM network for rumor veracity estimation of tweets
Twitter has become a fertile place for rumors, as information can spread to a large number of
people immediately. Rumors can mislead public opinion, weaken social order, decrease the …
people immediately. Rumors can mislead public opinion, weaken social order, decrease the …
Detection of spam reviews: a sentiment analysis approach
Electronic shopping is highly influenced by online reviews posted by customers against the
product quality. Some fraudulent pretenders consider this as an opportunity to write the …
product quality. Some fraudulent pretenders consider this as an opportunity to write the …
A comparative analysis of machine learning techniques for disaster-related tweet classification
Disaster-related tweets on Twitter during emergencies contain various information about
injured or dead people, missing or found people, infrastructure and utility damage that can …
injured or dead people, missing or found people, infrastructure and utility damage that can …
GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules
Extracting precise location information from microblogs is a crucial task in many
applications, particularly in disaster response, revealing where damages are, where people …
applications, particularly in disaster response, revealing where damages are, where people …
Disaster related social media content processing for sustainable cities
The current study offers a hybrid convolutional neural networks (CNN) model that filters
relevant posts and categorises them into several humanitarian classifications using both …
relevant posts and categorises them into several humanitarian classifications using both …
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster
During a disaster, a large number of disaster-related social media posts are widely
disseminated. Only a small percentage of disaster-related information is posted by …
disseminated. Only a small percentage of disaster-related information is posted by …
Deep neural networks for location reference identification from Bilingual disaster-related tweets
Twitter is increasingly being used during disasters to communicate with authorities,
ascertain the ground reality, and coordinate real-time rescue and recovery activities …
ascertain the ground reality, and coordinate real-time rescue and recovery activities …