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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Online-Zugang: | https://arxiv.org/abs/2312.16490 |
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| _version_ | 1866911007856656384 |
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| author | Wang, Zhengjia Wang, Danding Sheng, Qiang Cao, Juan Ma, Siyuan Cheng, Haonan |
| author_facet | Wang, Zhengjia Wang, Danding Sheng, Qiang Cao, Juan Ma, Siyuan Cheng, Haonan |
| contents | Understanding the intent behind information is crucial. However, news as a medium of public discourse still lacks a structured investigation of perceived news intent and its application. To advance this field, this paper reviews interdisciplinary studies on intentional action and introduces a conceptual deconstruction-based news intent understanding framework (NINT). This framework identifies the components of intent, facilitating a structured representation of news intent and its applications. Building upon NINT, we contribute a new intent perception dataset. Moreover, we investigate the potential of intent assistance on news-related tasks, such as significant improvement (+2.2% macF1) in the task of fake news detection. We hope that our findings will provide valuable insights into action-based intent cognition and computational social science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_16490 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring news intent and its application: A theory-driven approach Wang, Zhengjia Wang, Danding Sheng, Qiang Cao, Juan Ma, Siyuan Cheng, Haonan Computation and Language Artificial Intelligence Computers and Society Understanding the intent behind information is crucial. However, news as a medium of public discourse still lacks a structured investigation of perceived news intent and its application. To advance this field, this paper reviews interdisciplinary studies on intentional action and introduces a conceptual deconstruction-based news intent understanding framework (NINT). This framework identifies the components of intent, facilitating a structured representation of news intent and its applications. Building upon NINT, we contribute a new intent perception dataset. Moreover, we investigate the potential of intent assistance on news-related tasks, such as significant improvement (+2.2% macF1) in the task of fake news detection. We hope that our findings will provide valuable insights into action-based intent cognition and computational social science. |
| title | Exploring news intent and its application: A theory-driven approach |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2312.16490 |