Abstract
A social issue is what arises when the public discuss a specific event. Recently, many large Internet based service companies provide new trends services that display the emerging issues based on their data, for example, Google displays “top 10 most searched topics” every hour. Those emerging issues reflect the trend of public interest. Forecasting those issues helps the user to prepare for the future. In this paper, we present our research on proposing the social issue-forecasting model. To do so, we first collected social issue keyword from Google Trends for 3 months since it is based on the large scale of public data. We apply the k-nearest neighbor algorithm, which is the pattern recognition technology for recognizing the complex patterns and trends. To improve the accuracy, we applied Ripple Down Rules.
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References
Ajith, A., Baikunth, N., Mahanti, P.K.: Hybrid intelligent systems for stock market analysis. In: Proceedings of International Conference on Computational Science (2003a)
Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications 36(3), 5932–5941 (2009)
Carneiro, H.A., Mylonakis, E.: Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases 49(10), 1557–1564 (2009)
Chatfield_Ch_Tim_Series_Forecasting, 1st edn. (280) (2000)
Earle, P., et al.: OMG Earthquake! Can Twitter improve earthquake response? Seismological Research Letters 81(2), 246–251 (2010)
Ginsberg, J., et al.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012–1014 (2008)
Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computer and Operations Research 32, 2513–2522 (2005)
Hughes, A.L., Palen, L.: Twitter adoption and use in mass convergence and emergency events. International Journal of Emergency Management 6(3), 248–260 (2009)
Kwak, H., et al.: What is Twitter, a social network or a news media? ACM (2010)
McCarthy, M.J.: Internet monitoring of suicide risk in the population. Journal of Affective Disorders 122(3), 277–279 (2010)
Page, A., Chang, S.S., Gunnell, D.: Surveillance of Australian Suicidal Behaviour Using the Internet? Australian and New Zealand Journal of Psychiatry 45(12), 1020–1022 (2011)
Pai, P.-F., Lin, C.-S.: A Hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33(6), 497–505 (2005)
Rech, J.: Discovering trends in software engineering with google trend. ACM SIGSOFT Software Engineering Notes 32(2), 1–2 (2007)
Thawornwong, S., Enke, D.: The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing 56, 205–232 (2004)
Twitter study. Technical report, Pear Analytics (August. 2009)
Taylor, J.W., Buizza, R.: Neural network load forecasting with weather ensemble predictions. IEEE Transactions on Power Systems 17(3), 626–632 (2002)
Barbounis, T.G., Theocharis, J.B., Alexiadis, M.C., Dokopoulos, P.S.: Longterm wind speed and power forecasting using local recurrent neural network models. IEEE Trans. Energy Convers 21(1), 273–284 (2006)
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Han, S.C., Chung, H., Kang, B.H. (2012). It Is Time to Prepare for the Future: Forecasting Social Trends. In: Kim, Th., Ma, J., Fang, Wc., Zhang, Y., Cuzzocrea, A. (eds) Computer Applications for Database, Education, and Ubiquitous Computing. EL DTA 2012 2012. Communications in Computer and Information Science, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35603-2_48
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DOI: https://doi.org/10.1007/978-3-642-35603-2_48
Publisher Name: Springer, Berlin, Heidelberg
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