144 citations · 569 across the 14 of their papers we have counts for
16 papers
RweetMiner: Automatic identification and categorization of help requests on twitter during disasters
Irfan Ullah, Sharifullah Khan, Muhammad Imran +1
Catastrophic events create uncertain situations for humanitarian organizations locating and providing aid to affected people. Many people turn to social media during disasters for…
A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria
Firoj Alam, Ferda Ofli, Muhammad Imran +1
People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twi…
Domain Adaptation with Adversarial Training and Graph Embeddings
Firoj Alam, Shafiq Joty, Muhammad Imran
The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems.…
CrisisMMD: Multimodal Twitter Datasets from Natural Disasters
Firoj Alam, Ferda Ofli, Muhammad Imran
During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, in…
Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
Dat Tien Nguyen, Firoj Alam, Ferda Ofli +1
The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relie…
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting
Muhammad Imran, Sanjay Chawla, Carlos Castillo
An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date. In this paper, we propose an innovativ…