臺灣博碩士論文加值系統

「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.

摘要 I
ABSTRACT II
1 INTRODUCTION 1
2 RELATED WORK 4
2.1 POLITICAL COMMUNICATION 4
2.2 COMMUNICATION 6
2.3 OPINION DYNAMICS 10
3 MODEL 12
3.1 BUILD THE NETWORK 12
3.2 MEDIA 14
3.3 INTERPERSONAL INTERACTION 16
3.4 FEATURE OF POLITICAL POLARIZATION 18
4 EXPERIMENTS AND ANALYSES 21
4.1 OPINION CHANGING RULES 21
4.2 AMOUNT OF MEDIA 25
4.3 MEDIA BIAS 26
5 CONCLUSION 30
APPENDIX A 補充實驗 31
REFERENCE 36

[1] http://www.pollingreport.com/2004.htm
[2] http://210.69.23.140/Excel/chart41.xls
[3] http://www.washingtonpost.com/ac2/wp-dyn/A39044-2004Apr24
[4] Moscovici, S. & Zavalloni, M., “The group as a polarizer of attitude”, Journal of Personality and Social Psychology 12, 125-135 1969
[5] Nigel Gilbert., “Simulation: A new way of doing social science”, The American Behavioral Scientist. Thousand Oaks: Aug 1999.Vol.42, Iss. 10; pg. 1485, 3 pgs
[6] K. Sznajd-Weron and J. Sznajd, Opinion evolution in closed community, Int. J. Mod. Phys. C 11, 6 (2000).
[7] AXELROD, R., “The Dissemination of Culture: A Model with Local Convergence and Global Polarization”, J. Conflict Resolut. 41, pp. 203-226; The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton University Press, Princeton NJ. 1997
[8] Ray Hiebert, Donald Unqurait, Thomas Bohn, “Mass Media: An Introduction to Modern Communication”, New York: David Mckay, 1974
[9] Guillaume Deffuant, Frédéric Amblard, Gérard Weisbuch and Thierry Faure, “How can extremism prevail? A study based on the relative agreement interaction model”, Journal of Artificial Societies and Social Simulation vol. 5, no. 4 2002
[10] von Neumann Neighborhood, http://mathworld.wolfram.com/vonNeumannNeighborhood.html
[11] Dietrich Stauffer, Adriano Sousa and Christian Schulze, “Discretized Opinion Dynamics of The Deffuant Model on Scale-Free Networks”, Journal of Artificial Societies and Social Simulation vol. 7, no. 3 2004
[12] Fang Wu and Bernardo A. Huberman , “Social Structure and Opinion Formation”, arXiv:cond-mat/0407252 v3 20 Jul 2004
[13] NOWAK A and Lewenstein M (1996) “Modeling social change with cellular automata”. In HEGSELMANN R, Mueller U and Troitzsch K G (Eds.) Modeling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Dordrecht, Kluwer: pp. 249-286.
[14] Hegselmann, R. and Krause, U., “Opinion dynamics and Bounded Confidence Models”, Analysis and Simulation, Journal of Artificial Societies and Social Simulation vol. 5, no. 3 2002
[15] Jager, W. and Amblard, F., 2004, "A dynamical perspective on attitude change", accepted to NAACSOS Conference, Pittsburgh, 2004
[16] K. Sznajd-Werona, R. Weronb, “How effective is advertising in duopoly markets?” Physica A 324 (2003) 437–444
[17] Ana Proykova and Dietrich Stauffer, “Social Percolation and the Influence of Mass Media”, arXiv:cond-mat/0203375 v1 19 Mar 2002
[18] P.J. Lazarsfeld, B. Berelson & H. Gaudent, “The People’s choice”, New York: Harper & Row, 1944
[19] http://www.usatoday.com/news/politicselections/front.htm
[20] G. Deffuant, F. Amblard and G. Weisbuch, (2004), “Modelling group opinion shift to extremism: the smooth bounded confidence model”, Proceedings of Vallalodid ESSA 2004 conference
[21] Santo Fortunato, “The Sznajd Consensus Model with Continuous Opinions”, arXiv:cond-mat/0407353 v1 14 Jul 2004
[22] NEWMAN M E J and Watts D J (1999) “Renormalization Group Analysis of the Small-world Network Model”. Physics Letters A, 263, pp. 341-346.
[23] http://www.brookings.edu/comm/policybriefs/pb139.htm
[24] 彭懷恩, 進階政治學Ｑ＆Ａ
[25] 李茂政, 人類傳播行為通論
[26] W. Schramm, “Man, Messages and Media”, New Work: Harper & Row, Publishers, 1973.
[27] Harold D Lasswell, (1948). “The Structure and Function of Communication in Society”, in Lyman Bryson, ed, The Communication of Idea, New York: Harper & Row, 1948. p37
[28] McQuail, Denis and Sven Windahl, “Communication Models for the Study of Mass Communication”, Longman, New York, 1981.
[29] Heinemann, G. Mersham and C. Skinner, “New Insights into Communication and Public Relations”, 1999.
[30] Frederic Amblard, Guillaume Deffuant, “The role of network topology on extremism propagation with the Relative Agreement opinion dynamics”, arXiv:cond-mat/0404574 v1 23 Apr 2004
[31] Johannes Kottonau and Claudia Pahl-Wostl, “Simulating political attitudes and voting behavior”, Journal of Artificial Societies and Social Simulation vol. 7, no. 4
[32] The Washington Post Company - Business Properties, www.washpostco.com/business-newspapers.htm