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<p>This paper introduces a mathematical framework to analyze news propagation and influence. We develop a software tool to crawl news websites and construct a temporal graph model of influence between outlets based on content similarity and publication times. Our primary contribution is the formulation of novel Budgeted Influence Maximization problems on these temporal grapfhs, which aim to identify how a limited set of news sources can achieve maximum influence over the network. We prove these problems are NP-hard and establish hardness-of-approximation results. To validate our approach, we solve these problems as integer programs on both real-world data and synthetic data from a statistically-validated random generator. Our key finding is that a resource budget of 40-60% of the total network cost is sufficient to achieve over 75% of the maximum possible influence. This work provides a quantitative framework and practical tools for analyzing influence and vulnerability in news ecosystems.</p>

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Towards understanding news plagiarism: theoretical and experimental analysis

  • 期刊论文

  • 发表日期：

    2025年12月2日

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  • Ruxandra Marinescu-Ghemeci,
  • Adrian Miclăuş,
  • Ionuț Murarețu,
  • Alexandru Popa

摘要

This paper introduces a mathematical framework to analyze news propagation and influence. We develop a software tool to crawl news websites and construct a temporal graph model of influence between outlets based on content similarity and publication times. Our primary contribution is the formulation of novel Budgeted Influence Maximization problems on these temporal grapfhs, which aim to identify how a limited set of news sources can achieve maximum influence over the network. We prove these problems are NP-hard and establish hardness-of-approximation results. To validate our approach, we solve these problems as integer programs on both real-world data and synthetic data from a statistically-validated random generator. Our key finding is that a resource budget of 40-60% of the total network cost is sufficient to achieve over 75% of the maximum possible influence. This work provides a quantitative framework and practical tools for analyzing influence and vulnerability in news ecosystems.