Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning (2021.emnlp-main)
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| Challenge: | Various deep learning models have been successfully employed for this type of NLP task of text classification. |
| Approach: | They propose a mixed-domain transfer learning approach that only captures local context and exhibits poor generalization. |
| Outcome: | The proposed model captures local and global contexts, but lacks generalization . a combination of shallow network-based domain-specific models and convolutional neural networks can extract local and globally context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution. |
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| Challenge: | Social media is used by individuals and organisations as a platform to spread misinformation. |
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Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
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CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter (2021.acl-srw)
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| Challenge: | myths, sensationalism, rumours and misinformation, generated intentionally or unintentionally, spread rapidly through social networks during the COVID-19 pandemic . evaluation of tweets for recognizing misinformation can create beneficial understanding to review the top quality and also the readability of online information concerning the COV-19. |
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| Challenge: | Existing methods for misinformation detection are limited by data scarcity . existing methods fail to detect early-stage misinformation on emerging topics . |
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| Challenge: | a multilingual dataset of COVID-19 vaccine misinformation is available from Brazil, Indonesia, and Nigeria. |
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| Challenge: | 3,000 English tweets labeled with emotions are used to predict emotions during crises . authors propose semi-supervised learning to bridge this gap . |
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| Challenge: | Graph Neural Networks (GNNs) are used to train neural networks to detect fake news based on context-based methods. |
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MisinfoEval: Generative AI in the Era of “Alternative Facts” (2024.emnlp-main)
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| Challenge: | Existing efforts to address misinformation on social media platforms are hampered by user biases and scalability challenges. |
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Multimodal Pipeline for Collection of Misinformation Data from Telegram (2022.lrec-1)
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| Challenge: | a large portion of misinformation is spread via multimodal means, such as images and videos . a new pipeline for collecting misinformation from Telegram allows us to collect a greater variety of mis-information examples . |
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Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation (2022.naacl-main)
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| Challenge: | Detecting out-of-context media is a problem in domains of public significance . a method that leverages automatically generated hard image-text mismatches is proposed . |
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