Exploring news intent and its application: A theory-driven approach
This work addresses the problem of understanding news intent for computational social science, but it is incremental as it builds on existing interdisciplinary studies.
The paper tackles the lack of structured investigation into perceived news intent by introducing a conceptual deconstruction-based framework (NINT) and a new intent perception dataset, resulting in a significant improvement of +2.2% macF1 in fake news detection.
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.