Beyond the Battlefield: Framing Analysis of Media Coverage in Conflict Reporting

2025-06-12

AI Summary

Existing research on conflict reporting suffers from insufficient identification of war/peace framing and overreliance on either qualitative analysis or overly generalized approaches. Method: This study proposes a novel integrative method combining frame semantics with large language models (LLMs), enabling, for the first time, joint quantitative identification of communicative and linguistic frames. Leveraging a multi-source news corpus on the Israel–Palestine conflict, we employ LLM fine-tuning, prompt engineering, and corpus-driven analysis to systematically extract war/peace journalism indicators and uncover geopolitical narrative biases. Contribution/Results: Findings reveal a statistically significant mainstream media bias toward the war frame; U.S., U.K., and Middle Eastern outlets exhibit divergent, statistically robust tendencies in perpetrator/victim attribution. The proposed framework offers a reproducible, scalable computational pathway for detecting reporting bias in conflict coverage, advancing media framing research toward deep semantic analysis and cross-regional comparative inquiry.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Bias, Fairness & Privacy
• Knowledge Representation and Reasoning: Preferences

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Framing used by news media, especially in times of conflict, can have substantial impact on readers' opinion, potentially aggravating the conflict itself. Current studies on the topic of conflict framing have limited insights due to their qualitative nature or only look at surface level generic frames without going deeper. In this work, we identify indicators of war and peace journalism, as outlined by prior work in conflict studies, in a corpus of news articles reporting on the Israel-Palestine war. For our analysis, we use computational approaches, using a combination of frame semantics and large language models to identify both communicative framing and its connection to linguistic framing. Our analysis reveals a higher focus on war based reporting rather than peace based. We also show substantial differences in reporting across the US, UK, and Middle Eastern news outlets in framing who the assailant and victims of the conflict are, surfacing biases within the media.

Problem

• Analyzes media framing impact on conflict perception
• Identifies war vs peace journalism indicators computationally
• Reveals regional media biases in conflict reporting

Innovation

• Computational frame semantics for conflict analysis
• Large language models detect linguistic framing
• Cross-regional media bias comparison