mlsanityData to 7 Oct 2026

Exploring news intent and its application: A theory-driven approach

arXiv 2312.16490 ↗First posted 27 Dec 2023cs.CL cs.AI cs.CYcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 100 references that are in the map. First posted on arXiv 27 Dec 2023.

Builds on

30 of 100 references.

References · 30

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18 more
  1. 2023Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detectioncited by 69
  2. 2020Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Surveycited by 51
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2024A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterizationcited by 18
  2. 2024Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Modelscited by 16
  3. 2024Why Misinformation is Created? Detecting them by Integrating Intent Featurescited by 11
  4. 2025Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Modelscited by 5
  5. 2025Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inferencecited by 3
  6. 2026What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previewscited by 3
  7. 2025Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detectioncited by 2