MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media

• URL: http://arxiv.org/abs/2604.20982v1
• Date: Wed, 22 Apr 2026 18:10:22 GMT
• Title: MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media
• Abstract summary: We present MediaGraph, a network-theoretic framework for analyzing reporting preferences in news media through entity co-occurrence networks.<n>We analyze these networks along three network theoretic axes of centrality, community structure, and co-occurrence link predictability.<n>Our results reveal significant differences in reporting preferences across sources for the same event, and a consistent under-representation of farmer leaders across sources.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present MediaGraph, a network-theoretic framework for analyzing reporting preferences in news media through entity co-occurrence networks. Using articles from four Indian news-sources, two mainstream (The Times of India and The Indian Express) and two fringe outlets (dna and firstpost), we construct source-specific co-occurrence networks around the 2020-21 and 2024 Farmers Protests. We analyze these networks along three network theoretic axes of centrality, community structure, and co-occurrence link predictability. The link predictability metric is a novel metric proposed that quantifies the consistency of entity associations over time using a GraphSAGE-based model. Our results reveal significant differences in reporting preferences across sources for the same event, and a consistent under-representation of farmer leaders across sources. By shifting the focus from textual signals to relational structures, our approach offers a scalable, label-independent perspective on media analysis and introduces link predictability as a complementary measure of reporting behavior.

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