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

TL;DR

MediaGraph introduces a network-based framework to analyze Indian news media's reporting preferences by examining entity co-occurrence networks, revealing biases and consistency in coverage across different outlets.

Contribution

The paper presents a novel network-theoretic approach with a new link predictability metric to analyze media reporting patterns beyond textual analysis.

Findings

Significant differences in reporting preferences across news sources.

Consistent under-representation of farmer leaders in coverage.

Link predictability effectively measures entity association stability.

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.