Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2312.16490 v2 pith:GMP72DOM submitted 2023-12-27 cs.CL cs.AIcs.CY

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

Zhengjia Wang , Danding Wang , Qiang Sheng , Juan Cao , Siyuan Ma , Haonan Cheng This is my paper
classification cs.CL cs.AIcs.CY
keywords intentnewsapplicationframeworknintstructuredunderstandingaction
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives

    cs.CL 2026-04 unverdicted novelty 6.0

    A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.