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Found 8 papers, 4 papers with code
Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup
Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management.
On Identifying Hashtags in Disaster Twitter Data
Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response.
Multimodal Semi-supervised Learning for Disaster Tweet Classification
In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events.
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations.
Keyphrase Extraction from Disaster-related Tweets
Previously, joint training of two different layers of a stacked Recurrent Neural Network for keyword discovery and keyphrase extraction had been shown to be effective in extracting keyphrases from general Twitter data.
Stance Detection in COVID-19 Tweets
The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e. g. stay at home mandates and wearing face masks when out in public.
Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event
Training a robust and reliable deep learning model requires a large amount of data.
Identification of Fine-Grained Location Mentions in Crisis Tweets
Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information.
