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

2026-04-22

AI Summary

This study investigates systematic disparities in coverage between mainstream and marginal Indian news outlets during the 2020–21 and 2024 farmer protests, particularly regarding the underrepresentation of key actors such as farmer leaders. By constructing entity co-occurrence–based media networks and integrating GraphSAGE with complex network analysis, the research characterizes media behavior through three structural dimensions: centrality, community structure, and a novel metric proposed herein—link predictability. This new indicator enables scalable, unsupervised media analysis solely from relational structures, without reliance on textual labels. Findings reveal a consistent and significant underestimation of farmer leaders’ visibility across diverse media outlets, exposing deep-seated structural biases in protest coverage.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Reasoning under Uncertainty: Causality

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• media bias
• reporting preferences
• entity co-occurrence
• news media analysis
• farmers protests

Innovation

• entity co-occurrence network
• link predictability
• GraphSAGE
• media bias analysis
• network-theoretic framework