MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914500491345920 |
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| author | Bali, Aditya Rupsha Kaushik, Vidur Sen, Anirban |
| author_facet | Bali, Aditya Rupsha Kaushik, Vidur Sen, Anirban |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20982 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media Bali, Aditya Rupsha Kaushik, Vidur Sen, Anirban Social and Information Networks Computers and Society 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. |
| title | MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media |
| topic | Social and Information Networks Computers and Society |
| url | https://arxiv.org/abs/2604.20982 |