MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media
For media analysts and researchers, it provides a scalable, label-independent framework to quantify reporting biases, though the findings are limited to two events and four Indian sources.
MediaGraph uses entity co-occurrence networks to analyze reporting preferences in Indian news media, revealing significant differences across sources and consistent under-representation of farmer leaders during the 2020-21 and 2024 Farmers Protests.
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.