Papers by Hamid Karimi
Multi-Source Multi-Class Fake News Detection (C18-1)
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| Challenge: | detecting fake news is challenging especially in the era of social media, as it is written intentionally to mislead readers. |
| Approach: | They propose a framework to combine information from multiple sources and discriminate between different degrees of fakeness. |
| Outcome: | The proposed framework can detect fake news with different degrees of fakeness . it integrates information from multiple sources and discriminates between them . |
Learning Hierarchical Discourse-level Structure for Fake News Detection (N19-1)
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| Challenge: | Existing methods for capturing discourse-level structure of fake news articles rely on annotated corpora. |
| Approach: | They propose to incorporate hierarchical discourse-level structure of fake and real news articles into detection methods . they propose to learn and construct a discourse- level structure for fake/real news articles . |
| Outcome: | The proposed approach can detect fake news articles based on their contents . it can also identify structure-related properties that can boost fake news understating . |
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification (2021.findings-acl)
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| Challenge: | Existing studies on text classification have focused on the bias towards the individuals mentioned in the text content. |
| Approach: | They propose a framework to mitigate implicit bias in text classification models based on demographic attributes of authors . they propose to use this framework to train deep text classifiers to make predictions on the right features . |
| Outcome: | The proposed framework outperforms existing models significantly in fairness and performance. |