Papers with Fake news detection
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection (2023.acl-industry)
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| Challenge: | Existing work on fake news detection does not consider the temporal shift issue caused by the rapidly-evolving nature of news data. |
| Approach: | They propose a framework to forecast temporal patterns of news data and guide detector to fast adapt to future distributions. |
| Outcome: | The proposed framework forecasts temporal distribution patterns and guides detector to fast adapt to future distribution. |
Zoom Out and Observe: News Environment Perception for Fake News Detection (2022.acl-long)
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| Challenge: | Existing methods for fake news detection "zoom in" to verify content with knowledge sources or check readers’ replies to posts but neglect information in the external news environment where a fake news post is created and disseminated. |
| Approach: | They propose a framework to capture news environment signals and a module to perceive useful signals and assist final prediction. |
| Outcome: | The proposed framework can improve the performance of basic fake news detectors by capturing the environmental signals of news posts and analyzing the results. |
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 . |
Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods to detect fake news focus on mining lexical and syntactic features. |
| Approach: | They propose a topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks to identify fake news on social media. |
| Outcome: | The proposed method outperforms state-of-the-art methods on real-world datasets. |
A Survey on Natural Language Processing for Fake News Detection (2020.lrec-1)
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| Challenge: | Automated fake news detection is a critical but challenging problem in NLP . social media has accelerated the spread of fake news, threatening public safety . |
| Approach: | They describe the challenges involved in fake news detection and describe related tasks . they outline promising research directions and highlight the difference between fake news and related tasks. |
| Outcome: | The proposed models are more fine-grained, detailed, fair, and practical. |