References
- Achrekar, H; Gandhe, A; Lazarus, R; Yu, S. H; Liu, B. 2011, Predicting flu trends using twitter data. Paper presented at the Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on.
- Backstrom, L; Sun, E; Marlow, C. 2010, Find me if you can: improving geographical prediction with social and spatial proximity. Paper presented at the Proceedings of the 19th international conference on World wide web.
- Bae, H. W; Bang, S. W. 2013, (with R) Discriminant analysis and Logistic Regression analysis, Kyowoosa, Seoul
- Bollen, J; Mao, H; Pepe, A. 2011, Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. Paper presented at the ICWSM.
- Cheng, Z; Caverlee, J; Lee, K. 2010, You are where you tweet: a content-based approach to geo-locating twitter users. Paper presented at the Proceedings of the 19th ACM international conference on Information and knowle
- Choi, H; Varian, H. 2012, Predicting the present with google trends. Economic Record, 88(s1): 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
- Davis Jr, C. A; Pappa, G. L; de Oliveira; D. R. R; de L Arcanjo, F. 2011, Inferring the location of Twitter messages based on user relationships. Transactions in GIS, 15(6):735-751. https://doi.org/10.1111/j.1467-9671.2011.01297.x
- Fujisaka, T; Lee, R; Sumiya, K. 2010, Discovery of user behavior patterns from geo-tagged microblogs. Paper presented at the Proceedings of the 4th International Conference on Uniquitous Information Management and Commdgemanagement.
- Ghosh, D; Guha, R. 2013, What are we 'tweeting' about obesity? Mapping tweets with topic modeling and Geographic Information System. Cartography and Geographic Information Science, 40(2):90-102. https://doi.org/10.1080/15230406.2013.776210
- Hecht, B; Hong, L; Suh, B; Chi, E. H. 2011, Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing.
- Ikawa, Y; Enoki, M; Tatsubori, M. 2012, Location inference using microblog messages. Paper presented at the Proceedings of the 21st international conference companion on World Wide Web.
- Kent, J. D; Capello Jr, H. T. 2013, Spatial patterns and demographic indicators of effective social media content during theHorsethief Canyon fire of 2012. Cartography and Geographic Information Science, 40(2):78-89. https://doi.org/10.1080/15230406.2013.776727
- Kim, K. S; Kim, H.T. 2008, Analysis on the Excess Commuting Travel Time, Korea Development Institute.
- Kim, S. J. 2013, Analyzing political attitudes of Twitter users by extracting sentiment from user timeline, The Catholic University of Korea, Bucheon.
- Kim, Y. H; Shin, S. 2013, The current status of use SNS in Korea, Korea Information Society Development Institute.
- Kwak, H; Lee, C; Park, H ; Moon, S. 2010, What is Twitter, a social network or a news media? Paper presented at the Proceedings of the 19th international conference on World wide web.
- Lee, D. W; Kang, H. K; Kim, S. H; Lee, C. M. 2013, Autocorrelation Analysis of the Sentiment with Stock Information Appearing on Big-Data. The Korean Journal Of Financial Engineering, 12(2):79-96.
- Lee, H. S; Lim, J. H. 2013, SPSS 20.0 Manual, Zip-hyunjae, Seoul.
- Li, L; Goodchild, M. F; Xu, B. 2013, Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and Geographic Information Science, 40(2):61-77. https://doi.org/10.1080/15230406.2013.777139
- Mayer-Schonberger, V; Cukier, K. 2013, Big data: A revolution that will transform how we live, work, and think: Houghton Mifflin Harcourt.
- Mitchell, L; Frank, M. R; Harris, K. D; Dodds, P. S; Danforth, C. M. 2013, The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PloS one, 8(5):e64417. https://doi.org/10.1371/journal.pone.0064417
- Noulas, A; Scellato, S; Mascolo, C; Pontil, M. 2011, An Empirical Study of Geographic User Activity Patterns in Foursquare. ICWSM, 11:70-573.
- Park, D. Y; Park, D. J. 2013, R and Statistical analysis, Jayu Academy, Paju.
- Roick, O; Heuser, S. 2013, Location Based Social Networks-Definition, Current State of the Art and Research Agenda. Transactions in GIS.
- Seo, T. W. 2012, A Study of Real-time Disaster Information Extraction and Displayusing the Mash-up based on SNS, Bukyung University, Busan.
- Sung, T. J. 2014, (Using SPSS/AMOS/HLM) Easy Statistical Analysis, HakJeeSa, Seoul.
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