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Predicting how a user responds to news events enables important applications such as allowing intelligent agents or content producers to estimate the effect on different communities and revise unreleased messages to prevent unexpected bad outcomes such as social conflict and moral injury.
On the variance of the adaptive learning rate and beyond
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Pytorch: An imperative style, high-performance deep learning library
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Read, attend and comment: A deep architecture for automatic news comment generation
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Transformers: State-of-the-art natural language processing
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Latent hatred: A benchmark for understanding implicit hate speech
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Infosurgeon: Cross-media fine-grained information consistency checking for fake news detection
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Semeval-2018 task 1: Affect in tweets
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Partner personas generation for dialogue response generation
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Leveraging users’ social network embeddings for fake news detection on twitter
Ting Su, Craig Macdonald, and Iadh Ounis. 2022 · 2022
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