{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T16:14:05Z","timestamp":1760199245516,"version":"build-2065373602"},"reference-count":13,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,6]],"date-time":"2018-11-06T00:00:00Z","timestamp":1541462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009023","name":"Precursory Research for Embryonic Science and Technology","doi-asserted-by":"publisher","award":["JPMJPR14DA"],"award-info":[{"award-number":["JPMJPR14DA"]}],"id":[{"id":"10.13039\/501100009023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP26310207"],"award-info":[{"award-number":["JP26310207"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each candidate word to illustrate the economic index. From this large volume of words, our method automatically selects the words which have strong correlation with the economic indicator and resolves some difficulties in statistics such as the spurious correlation and overfitting. As a result, our model reasonably illustrates the real economy index. The announcement of an economic index from government usually has a time lag, while our proposed method can be real time.<\/jats:p>","DOI":"10.3390\/e20110852","type":"journal-article","created":{"date-parts":[[2018,11,7]],"date-time":"2018-11-07T03:45:22Z","timestamp":1541562322000},"page":"852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimation of Economic Indicator Announced by Government From Social Big Data"],"prefix":"10.3390","volume":"20","author":[{"given":"Kenta","family":"Yamada","sequence":"first","affiliation":[{"name":"Institute of Innovative Research, Tokyo Institute of Technology, 4259, Nagatsuta-cho, Yokohama 226-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideki","family":"Takayasu","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research, Tokyo Institute of Technology, 4259, Nagatsuta-cho, Yokohama 226-8502, Japan"},{"name":"Sony Computer Science Laboratories, 3-14-13, Higashi-Gotanda, Shinagawa-ku, Tokyo 141-0022, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Misako","family":"Takayasu","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research, Tokyo Institute of Technology, 4259, Nagatsuta-cho, Yokohama 226-8502, Japan"},{"name":"Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, 4259, Nagatsuta-cho, Yokohama 226-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sano, Y., Yamada, K., Watanabe, H., Takayasu, H., and Takayasu, M. (2013). Empirical analysis of collective human behavior for extraordinary events in the blogosphere. Phys. Rev. E, 87.","DOI":"10.1103\/PhysRevE.87.012805"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fujiyama, T., Matsui, C., and Takemura, A. (2016). A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0160592"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Takayasu, M., Sato, K., Sano, Y., Yamada, K., Miura, W., and Takayasu, H. (2015). Rumor Diffusion and Convergence during the 3.11 Earthquake: A Twitter Case Study. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0121443"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Preis, T., Moat, H.S., and Stanley, H.E. (2013). Quantifying Trading Behavior in Financial Markets Using Google Trends. Sci. Rep., 3.","DOI":"10.1038\/srep01684"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"De Choudhury, M., Sundaram, H., John, A., and Seligmann, D.D. (2008, January 19\u201321). Can blog communication dynamics be correlated with stock market activity?. Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, Pittsburgh, PA, USA.","DOI":"10.1145\/1379092.1379106"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17486","DOI":"10.1073\/pnas.1005962107","article-title":"Predicting consumer behavior with Web search","volume":"107","author":"Goel","year":"2010","journal-title":"PNAS"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1111\/j.1475-4932.2012.00809.x","article-title":"Predicting the Present with Google Trends","volume":"88","author":"Choi","year":"2012","journal-title":"Econ. Rec."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sakaki, T., Okazaki, M., and Matsuo, Y. (2010, January 26\u201330). Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. Proceedings of the 19th International Conference on World Wide Web, New York, NY, USA.","DOI":"10.1145\/1772690.1772777"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/0304-4076(74)90034-7","article-title":"Spurious regressions in econometrics","volume":"2","author":"Granger","year":"1974","journal-title":"J. Econ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0304-4076(86)90001-1","article-title":"Understanding spurious regressions in econometrics","volume":"33","author":"Phillips","year":"1986","journal-title":"J. Econ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1214\/aos\/1176342871","article-title":"Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems","volume":"2","author":"Antoniak","year":"1974","journal-title":"Ann. Stat."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ohnishi, T., Mizuno, T., Aihara, K., Takayasu, M., and Takayasu, H. (2006). Systematic tuning of optimal weighted-moving-average of yen-dollar market data. Practical Fruits of Econophysics, Springer.","DOI":"10.1007\/4-431-28915-1_10"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/11\/852\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:28:18Z","timestamp":1760196498000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/11\/852"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,6]]},"references-count":13,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["e20110852"],"URL":"https:\/\/doi.org\/10.3390\/e20110852","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2018,11,6]]}}}