使用社會模擬分析傳媒在政治極化動態中的角色

Abstract

「政治極化」是近年來在許多民主國家中普遍存在的一種社會現象，頗受社會科學領域的學者，尤其是政治學家的重視。本研究從社會極化程度、意見群聚度和意見極化程度三個不同的層面來定義政治極化現象，並結合新興的社會科學電腦模擬與複雜網路理論二種研究工具來建立模擬模型，且探索政治極化的發展機制、形成過程與動態結果。我們擴充單一議題的意見動態模擬模型成為多議題的意見動態模擬模型，並充分利用人際接觸社會網路的小世界與無尺度性質，來模擬與分析媒體數量和報導內容在政治極化的發展過程中所扮演的關鍵角色。根據模擬模型的實驗結果，我們發現，新聞媒體渲染誇張與重口味的報導方式，是政治極化形成的一個重要因素，而我們也從模擬模型的敏感度分析實驗中發現，若能提昇新聞報導的公正性，將能有效減緩該社會的政治極化的形成與程度。

“Political polarization”, an arising phenomenon in many countries in recent years, arouses great attention of scholars in social sciences, especially in political science. Our research defines political polarization in terms of the degree of social polarization, the opinion clustering and the extremity of opinion. We build a model, by a combination of computer simulation in social science and complex network theory, and explore the mechanism, the process and the result of political polarization. We extend a single-issue model of opinion dynamics to a multiple-issue model, and incorporate characteristics in small-world networks and scale-free networks to simulate and analyze the role played by the amount of media and the media content. According to the results of the experiments, we find that exaggerated reporting is an important factor to political polarization. Moreover, from the sensitivity analysis, we conclude that if the fairness of news could be raised, the degree of political polarization will be reduced and the formation of political polarization will be slowed.