arXiv cs.CY· Hager Hesham Mustafa (Anmat Computational Analysis Lab)·· 12 小时前AI 评分34
用关联规则挖掘做无监督新闻框架检测：Framing Gaza 案例研究
Mining Frames: Association Rule Mining as an Unsupervised Framework for News Framing Detection
AI 导读
一篇 arXiv 工作论文提出用关联规则挖掘（ARM）作为无需标注的新闻框架检测框架，把文档或句子视为词汇项事务，以高提升度共现规则作为候选框架信号。
正文 · 原文
Abstract:Computational framing analysis is dominated by supervised classifiers that require large annotated corpora and predefined frame taxonomies, limiting their use for non-English languages, novel events, and low-resource settings. This paper presents Association Rule Mining (ARM) as an unsupervised, annotation-free framework for news framing detection. Each document or sentence is treated as a transaction of lexical items, and high-lift co-occurrence rules are read as candidate framing signals (Gamson and Modigliani's condensing symbols), confirmed in context by journalist-researchers. The primary validation is Framing Gaza: 25,512 English-language articles from 16 US, UK, French, and German outlets covering the first year of the Gaza war (October 2023 to October 2024), analyzed with Apriori (mlxtend), platform-weighted support, POS-filtered n-grams, and lift-based scoring. ARM inductively surfaces interpretable patterns without predefined categories: a cross-platform military frame, {israeli, strike} to {kill}, appears as a closed rule in 14 of 16 outlets, and a month-by-month analysis recovers event-driven framing pivots (e.g., the April 2024 World Central Kitchen strike) with no prior event labeling. A systematic headline-body divergence is identified, with headlines carrying concentrated, high-lift editorial framing and body text reproducing institutional, procedural language. A methodological validation on the Seven Days project (1,575 Arabic articles on the 2026 Iran war) shows that FP-Growth enables sentence-level analysis with far lower memory overhead. Findings were validated through editorial uptake: two published investigative stories (Daraj Media; Saheeh Masr). Preprocessing code is released openly; the full pipeline is documented but withheld to preserve operational deployability.
| Comments: | 35 pages, 5 figures, 4 tables. Working paper. Dataset: this https URL |
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR) |
| ACM classes: | I.2.7; H.2.8; J.4 |
| Cite as: | arXiv:2610.31874 [cs.CL] |
| (or arXiv:2610.31874v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31874 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hager Abdeltawab [view email]
[v1]
Thu, 22 Oct 2026 20:06:03 UTC (759 KB)
来源：arXiv cs.CY · arxiv.org