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

Yıl 2020, Cilt: 15 Sayı: 22, 823 - 839, 29.02.2020
https://doi.org/10.26466/opus.639650

Öz

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

Kaynakça

  • 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/2017HO-SandikveSecmenSayilari.pdf
  • Atikcan, E. O. and Oge, K. (2012). Referendum campaigns in polarized societies: the case of Turkey. Turkish Studies, 13(3), 449-470.
  • 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.
  • Bornsteina, N. and Lanz, B. (2008). Voting on the environment: price or ideology? Evidence from swiss referendums. Ecological Economics, 67, 430-440.
  • 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.
  • Closa, C. (2007). Why convene referendums? Explaining choices in eu constitutional politics. Journal of European Public Policy, 14(8), 1311–1332.
  • 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.
  • Darcy, R. and Laver, M. (1990). Referendum dynamics and the Irish divorce amendment. Public Opinion Quarterly, 54, 1-20.
  • Erikson, R. S., and Christopher W. (2008). Are political markets really superior to polls as election predictors?. Public Opinion Quarterly, 72(2), 190–215.
  • Erikson, R. S., and Christopher Wlezien, (2012). Markets vs. polls as election predictors: an historical assessment. Electoral Studies, 31(3), 532–539.
  • 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.
  • 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.
  • 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.
  • Foody, G.M. and Mathur, A., (2006). The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103, 179–189.
  • Graefe, A. (2014). Accuracy of vote expectation surveys in forecasting elections. Public Opinion Quarterly, 78(S1), 204–232.
  • Graefe, A., J. Scott A., Randall J. J., and Alfred G. C. (2014a). Combining forecasts: an application to elections. International Journal of Forecasting, 30(1), 43–54.
  • Graefe, A., J. Scott A., Randall J. J., and Alfred G. C. (2014b). Accuracy of combined forecasts for the 2012 presidential elections: the polly vote. PS: Political Science & Politics, 47(2), 427–431.
  • Higley, J. and Mcallister, I. (2002). Elite division and voter confusion: Australia’s republic referendum in 1999. European Journal of Political Research, 41, 845–861.
  • Hong, J., Min, J., Cho, U., and Cho, S., (2008). Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers. Pattern Recognition, 41, 662–671.
  • Hsu, C. W. and Lin, C-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13(2), 415–425.
  • Hummel, P. and Rothschild, D. (2014). Fundamental models for forecasting elections at the state level. Electoral Studies, 35, 123-139.
  • Huang, C., Davis, L.S., and Townshed, J.R.G., (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, 725–749.
  • Jerzak, Connor T. (2014). The EU’s democratic deficit and repeated referendums in Ireland. Int J Polit Cult Soc, 27, 367–388.
  • Kavzoglu, T., and Colkesen, I., (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352–359.
  • Laycock, S. (2013). Is referendum voting distinctive? Evidence from three UK cases. Electoral Studies, 32, 236–252
  • LeDuc, L. (2000). Referendums and elections: how do campaigns differ? Retrieved 01.02.2017 from https://ecpr.eu/Events/PaperDetails.aspx?PaperID=15003&EventID=46
  • LeDuc, L. (2002). Opinion change and voting behavior in referendums. European Journal of Political Research, 41(6), 711–732.
  • LeDuc, L. (2005). Saving the pound or voting for Europe? Expectations for referendums on the constitution and the Euro. Journal of Elections, Public Opinion and Parties, 15(2), 169-196.
  • LeDuc, L. (2015). Referendums and deliberative democracy. Electoral Studies, 38, 139-148.
  • Linzer, D. and Lewis-Beck, M. S. (2015). Forecasting US presidential elections: New approaches (an introduction). International Journal of Forecasting, 31, 895–897.
  • Lock, K., and Andrew G. (2010). Bayesian Combination of State Polls and Election Forecasts. Political Analysis, 18(3), 337–348.
  • Lau, K.W., and Wu, Q.H., (2008). Local prediction of non-linear time series using support vector regression. Pattern Recognition, 41, 1556–1564
  • Marques, A. and Smith, T. B. (1984). Referendums in the third world. Electoral Studies, 3(1), 85-105.
  • Neijens, P. and van Praag, P. (2006). The dynamics of opinion formation in local popular referendums: why the Dutch always say no. International Journal of Public Opinion Research, 18(4), 445-462.
  • Norpoth H. (2004). Forecasting British elections: a dynamic perspective. Electoral Studies, 23, 297–305.
  • O'Mahony, J. (2009). Ireland's EU referendum experience. Irish political studies, 24(4), 429-446.
  • Roberts‐Thomson, P. (2001). EU treaty referendums and the European Union. Journal of European Integration, 23(2), 105-137.
  • Song, X., Zheng D. and Jiang, X. (2012). Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image. International Journal of Remote Sensing, 33(10), 3301-3320.
  • Quinlan, S., Shephard, M. and Paterson, L. (2015). Online discussion and the 2014 scottish independence referendum: flaming keyboards or forums for deliberation? Electoral Studies, 38, 192-205.
  • Uste B. R. and Güzel, B. (2011). Referendums and e-voting in Turkey. International Journal of Ebusiness and Egovernment Studies, 3(1), 147-156
  • Wang, J. G., Neskovic, P., and Cooper, L. N. (2005). Training data selection for support vector machines. Lecture Notes in Computer Science, 3610, 554−564.
  • Zhu, G. and Blumberg, D.G., (2002). Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel. Remote Sensing of Environment, 80, 233–240.

Referandum Sonucunun Seçmenlerin Yerel Yönetime Tutumu İle Tahmin Edilmesi

Yıl 2020, Cilt: 15 Sayı: 22, 823 - 839, 29.02.2020
https://doi.org/10.26466/opus.639650

Öz

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.

Kaynakça

  • 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/2017HO-SandikveSecmenSayilari.pdf
  • Atikcan, E. O. and Oge, K. (2012). Referendum campaigns in polarized societies: the case of Turkey. Turkish Studies, 13(3), 449-470.
  • 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.
  • Bornsteina, N. and Lanz, B. (2008). Voting on the environment: price or ideology? Evidence from swiss referendums. Ecological Economics, 67, 430-440.
  • 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.
  • Closa, C. (2007). Why convene referendums? Explaining choices in eu constitutional politics. Journal of European Public Policy, 14(8), 1311–1332.
  • 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.
  • Darcy, R. and Laver, M. (1990). Referendum dynamics and the Irish divorce amendment. Public Opinion Quarterly, 54, 1-20.
  • Erikson, R. S., and Christopher W. (2008). Are political markets really superior to polls as election predictors?. Public Opinion Quarterly, 72(2), 190–215.
  • Erikson, R. S., and Christopher Wlezien, (2012). Markets vs. polls as election predictors: an historical assessment. Electoral Studies, 31(3), 532–539.
  • 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.
  • 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.
  • 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.
  • Foody, G.M. and Mathur, A., (2006). The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103, 179–189.
  • Graefe, A. (2014). Accuracy of vote expectation surveys in forecasting elections. Public Opinion Quarterly, 78(S1), 204–232.
  • Graefe, A., J. Scott A., Randall J. J., and Alfred G. C. (2014a). Combining forecasts: an application to elections. International Journal of Forecasting, 30(1), 43–54.
  • Graefe, A., J. Scott A., Randall J. J., and Alfred G. C. (2014b). Accuracy of combined forecasts for the 2012 presidential elections: the polly vote. PS: Political Science & Politics, 47(2), 427–431.
  • Higley, J. and Mcallister, I. (2002). Elite division and voter confusion: Australia’s republic referendum in 1999. European Journal of Political Research, 41, 845–861.
  • Hong, J., Min, J., Cho, U., and Cho, S., (2008). Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers. Pattern Recognition, 41, 662–671.
  • Hsu, C. W. and Lin, C-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13(2), 415–425.
  • Hummel, P. and Rothschild, D. (2014). Fundamental models for forecasting elections at the state level. Electoral Studies, 35, 123-139.
  • Huang, C., Davis, L.S., and Townshed, J.R.G., (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, 725–749.
  • Jerzak, Connor T. (2014). The EU’s democratic deficit and repeated referendums in Ireland. Int J Polit Cult Soc, 27, 367–388.
  • Kavzoglu, T., and Colkesen, I., (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352–359.
  • Laycock, S. (2013). Is referendum voting distinctive? Evidence from three UK cases. Electoral Studies, 32, 236–252
  • LeDuc, L. (2000). Referendums and elections: how do campaigns differ? Retrieved 01.02.2017 from https://ecpr.eu/Events/PaperDetails.aspx?PaperID=15003&EventID=46
  • LeDuc, L. (2002). Opinion change and voting behavior in referendums. European Journal of Political Research, 41(6), 711–732.
  • LeDuc, L. (2005). Saving the pound or voting for Europe? Expectations for referendums on the constitution and the Euro. Journal of Elections, Public Opinion and Parties, 15(2), 169-196.
  • LeDuc, L. (2015). Referendums and deliberative democracy. Electoral Studies, 38, 139-148.
  • Linzer, D. and Lewis-Beck, M. S. (2015). Forecasting US presidential elections: New approaches (an introduction). International Journal of Forecasting, 31, 895–897.
  • Lock, K., and Andrew G. (2010). Bayesian Combination of State Polls and Election Forecasts. Political Analysis, 18(3), 337–348.
  • Lau, K.W., and Wu, Q.H., (2008). Local prediction of non-linear time series using support vector regression. Pattern Recognition, 41, 1556–1564
  • Marques, A. and Smith, T. B. (1984). Referendums in the third world. Electoral Studies, 3(1), 85-105.
  • Neijens, P. and van Praag, P. (2006). The dynamics of opinion formation in local popular referendums: why the Dutch always say no. International Journal of Public Opinion Research, 18(4), 445-462.
  • Norpoth H. (2004). Forecasting British elections: a dynamic perspective. Electoral Studies, 23, 297–305.
  • O'Mahony, J. (2009). Ireland's EU referendum experience. Irish political studies, 24(4), 429-446.
  • Roberts‐Thomson, P. (2001). EU treaty referendums and the European Union. Journal of European Integration, 23(2), 105-137.
  • Song, X., Zheng D. and Jiang, X. (2012). Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image. International Journal of Remote Sensing, 33(10), 3301-3320.
  • Quinlan, S., Shephard, M. and Paterson, L. (2015). Online discussion and the 2014 scottish independence referendum: flaming keyboards or forums for deliberation? Electoral Studies, 38, 192-205.
  • Uste B. R. and Güzel, B. (2011). Referendums and e-voting in Turkey. International Journal of Ebusiness and Egovernment Studies, 3(1), 147-156
  • Wang, J. G., Neskovic, P., and Cooper, L. N. (2005). Training data selection for support vector machines. Lecture Notes in Computer Science, 3610, 554−564.
  • Zhu, G. and Blumberg, D.G., (2002). Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel. Remote Sensing of Environment, 80, 233–240.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yöneylem
Bölüm Makaleler
Yazarlar

H. Mustafa Paksoy 0000-0001-7975-1795

Mehmet Özçalıcı 0000-0003-0384-6872

B. Dilek Özbezek 0000-0001-7176-1534

Yayımlanma Tarihi 29 Şubat 2020
Kabul Tarihi 14 Şubat 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 15 Sayı: 22

Kaynak Göster

APA Paksoy, H. M., Özçalıcı, M., & Özbezek, B. D. (2020). Prediction of The Referendum Results According To People’s Attitude Towards The Local Government. OPUS International Journal of Society Researches, 15(22), 823-839. https://doi.org/10.26466/opus.639650