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Found 4 papers, 2 papers with code
Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
Automatic depression detection on Twitter can help individuals privately and conveniently understand their mental health status in the early stages before seeing mental health professionals.
RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media
Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available.
Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection
Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs).
Data Augmentation for Rumor Detection Using Context-Sensitive Neural Language Model With Large-Scale Credibility Corpus
We present an offline data augmentation method based on semantic relatedness for rumor detection.
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