Journals / OPUS Uluslararası Toplum Araştırmaları Dergisi / 2020 / Cilt: 15 - Sayı: 22

Prediction of The Referendum Results According To People's Attitude Towards The Local Government

Referandum Sonucunun Seçmenlerin Yerel Yönetime Tutumu İle Tahmin Edilmesi

Pages
823–839
Publication Date
2020-02-29
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This results guide the survey companies' efforts to predict the results of the referendum

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In recent years, the referendums play an important role in modern democracies.
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Key Findings & Data:For this purpose, a qualitative research was designed and 2446 voters were interviewed in Kilis Province Center of Turkey.
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Conclusion & Impact:This results guide the survey companies' efforts to predict the results of the referendum
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Destek Vektör Makineleri · Referandum · Tahmin

Abstract

In recent years, the referendums play an important role in modern democracies. With the rapid changes in globalization, information and communication technologies and the increasing complexity of economic and social problems caused by intense competition, especially the referendums that are held to maximize the legitimacy of the decisions taken by the public will are seen as direct participation of citizens in the political process when traditional forms of political participation are declining. This study present a method to predict how citizen will vote in the referendum only by starting from their attitude towards local governments. For this purpose, a qualitative research was designed and 2446 voters were interviewed in Kilis Province Center of Turkey. In the study, Support Vector Machines, which is a nonparametric classification method based on statistical learning theory, was used to estimate the referendum results that would be voted as yes or no. At the end of the study, by using twenty-six variables, the rate of people voting in the referendum was predicted by 81%. This results guide the survey companies' efforts to predict the results of the referendum

Özet

Son yıllarda referandumlar modern demokrasilerde önemli bir rol oynamaktadır. Küreselleşme, bilgi ve iletişim teknolojilerindeki hızlı değişimler ve yoğun rekabetin meydana getirdiği ekonomik ve sosyal problemlerin giderek daha karmaşık hale gelmesi ile birlikte kamu iradesinin aldığı kararların meşruluğunu en üst düzeye çıkarmak için kullanılan referandumlar, özelikle geleneksel siyasal katılım azaldığı dönemde vatandaşların siyasi sürece doğrudan katılımı olarak görülmektedir. Bu çalışmanın amacı vatandaşların sadece yerel yönetimlere karşı tutumlarından yola çıkarak referandumda ne yönde oy kullanacakları tahmin edilmesi için bir yöntem ortaya koymaktır. Bu amaç doğrultusunda nitel bir araştırma tasarlanmış ve Kilis İl Merkez’de 2446 adet seçmene anket uygulanmıştır. Çalışmada evet veya hayır yönünde oy kullanılacak referandum sonuçları tahmin edilmesinde istatistiksel öğrenme teorisine dayalı parametrik olmayan bir sınıflandırma yöntemi olan Destek Vektör Makineleri (Support Vector Machines) kullanılmıştır. Araştırma sonunda yirmi altı değişken kullanmak suretiyle, kişilerin referandumda hangi yönde oy kullanacakları %81 oranında önceden kestirilebilmiştir. Bu sonuçlar anket şirketlerinin referandum sonuçlarını önceden tahmin etme çabalarına yol gösterici niteliktedir.

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Cite This Article

PAKSOY, H. M., ÖZÇALICI, M., ÖZBEZEK, B. D. (2020). Prediction of The Referendum Results According To People's Attitude Towards The Local Government. OPUS Uluslararası Toplum Araştırmaları Dergisi, Cilt: 15 - Sayı: 22, 823–839. https://ajindex.com/articles/76943

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References (13)

  1. Kecman, V. (2001). Learning and soft computing, support vector machines, neural networks and fuzzy logic models. The MIT Press. yok.gov.tr, Retrieved 01.02.2017 from http://www.ysk.gov.tr/ysk/content/conn/YSKUCM/path/Contribution %20Folders/SecmenIslemleri/Secimler/2017HOSandikveSecmenSayilari.pdf Atikcan, E. O. and Oge, K. (2012). Referendum campaigns in polarized societies: the case of Turkey. Turkish Studies, 13(3), 449-470.
  2. Baum, M. A. and Freire, A. (2001). Political parties, cleavage structures and referendum voting: electoral behavior in the Portuguese regionalization referendum of 1998. South European Society and Politics, 6(1), 1-26.
  3. Bornsteina, N. and Lanz, B. (2008). Voting on the environment: price or ideology? Evidence from swiss referendums. Ecological Economics, 67, 430-440.
  4. Borges, W. and Clarke, H. D. (2008). Cues in context: analyzing the heuristics of referendum voting with an internet survey experiment. Journal of Elections, Public Opinion and Parties, 18(4), 433–448.
  5. Closa, C. (2007). Why convene referendums? Explaining choices in eu constitutional politics. Journal of European Public Policy, 14(8), 1311–1332.
  6. Campbell, J. E. (2001). The referendum that didn't happen: the forecasts of the 2000 presidential election. PS, Political Science & Politics, 34(1), 33-38.
  7. Darcy, R. and Laver, M. (1990). Referendum dynamics and the Irish divorce amendment. Public Opinion Quarterly, 54, 1-20.
  8. Erikson, R. S., and Christopher W. (2008). Are political markets really superior to polls as election predictors?. Public Opinion Quarterly, 72(2), 190–215.
  9. Erikson, R. S., and Christopher Wlezien, (2012). Markets vs. polls as election predictors: an historical assessment. Electoral Studies, 31(3), 532–539.
  10. Fisher, S. D. (2015). Predictable and unpredictable changes in party support: a method for long-range daily election forecasting from opinion polls. Journal of Elections, Public Opinion and Parties, 25(2), 137–158.
  11. Fisher, S. D. and Shorrocks, R. (2018). Collective failure? Lessons from combining forecasts for the UK's referendum on EU membership. Journal of Elections, Public Opinion and Parties, 28(1), 59-77.
  12. Foody, G.M. and Mathur, A. (2004). Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93, 107–117.
  13. Foody, G.M. and Mathur, A., (2006). The use of small training sets containing mixed pixels for accurate hard image classification: training on mixe