{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:47:50Z","timestamp":1754156870602,"version":"3.41.2"},"reference-count":9,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T00:00:00Z","timestamp":1610496000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2021,6,21]]},"abstract":"Purpose<\/jats:title>This paper aims to estimate the population in a specific space from the numbers of posted tweets and their senders, using Twitter's real-time property and location information data.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>The population to be estimated was set to be the attendance at each game among the six baseball teams of the Japan Professional Baseball Pacific League held at the main stadium of each team. The relation between the attendance and Twitter data was analyzed, and regression models using Twitter data were used to estimate the attendances.<\/jats:p><\/jats:sec>Findings<\/jats:title>The correlation coefficient tended to be larger for the attendance and tweeting users than for the attendance and that of the number of tweets. Furthermore, the comparison and evaluation of several regression models combining Twitter data, game data and weather data for estimating the attendance showed the usefulness of Twitter data, and that using the number of tweeting users improved the accuracy of population estimation.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>While there are many studies on event detection or location identification using Twitter data, no study has been reported on the estimation of the population in a specific space using \u201ctime information\u201d and \u201clocation information\u201d characteristic of Twitter data. Using Twitter data, which contains users' messages, for estimating the population can be extended to various types of analyses, such as the analysis of feelings and opinions of the groups in the space.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-03-2020-0065","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T05:04:25Z","timestamp":1610514265000},"page":"430-445","source":"Crossref","is-referenced-by-count":1,"title":["Population estimation using Twitter for a specific space"],"prefix":"10.1108","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9537-9882","authenticated-orcid":false,"given":"Hiroki","family":"Hara","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshikatsu","family":"Fujita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuhiko","family":"Tsuda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"first-page":"1568","article-title":"Twitter catches the flu: detecting influenza epidemics using Twitter","year":"2011","key":"key2021091708205466900_ref001"},{"issue":"1","key":"key2021091708205466900_ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","article-title":"Twitter mood predicts the stock market","volume":"2","year":"2011","journal-title":"Journal of Computational Science"},{"volume-title":"Bootstrap Methods and Their Application","year":"1997","key":"key2021091708205466900_ref003"},{"first-page":"1526","article-title":"A study of effectively finding tweets with location information","year":"2011","key":"key2021091708205466900_ref004"},{"article-title":"Behavioral analysis of social media users based on DNS queries","volume-title":"Ph.D. thesis, University of Tsukuba","year":"2014","key":"key2021091708205466900_ref005"},{"issue":"1","key":"key2021091708205466900_ref006","first-page":"67","article-title":"Twitter as a social sensor: can social sensors exceed physical sensors? ( Twitter and social media)","volume":"27","year":"2012","journal-title":"Journal of Japanese Society for Artificial Intelligence"},{"first-page":"851","article-title":"Earthquake shakes Twitter user: real-time event detection by social sensors","year":"2010","key":"key2021091708205466900_ref007"},{"key":"key2021091708205466900_ref008","first-page":"63","article-title":"Mobile spatial statistics: population estimation technology and applications using mobile phone networks","volume":"28","year":"2014","journal-title":"Journal of the Japanese Society of Computational Statistics"},{"issue":"4","key":"key2021091708205466900_ref009","first-page":"47","article-title":"Big data collection on Twitter","volume":"48","year":"2015","journal-title":"The Academic Association for Organizational Science"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-03-2020-0065\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-03-2020-0065\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:15:01Z","timestamp":1753398901000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/55\/3\/430-445\/81851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,13]]},"references-count":9,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,1,13]]},"published-print":{"date-parts":[[2021,6,21]]}},"alternative-id":["10.1108\/DTA-03-2020-0065"],"URL":"https:\/\/doi.org\/10.1108\/dta-03-2020-0065","relation":{},"ISSN":["2514-9288"],"issn-type":[{"type":"print","value":"2514-9288"}],"subject":[],"published":{"date-parts":[[2021,1,13]]}}}