{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:51:18Z","timestamp":1742914278701,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319312316"},{"type":"electronic","value":"9783319312323"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-31232-3_83","type":"book-chapter","created":{"date-parts":[[2016,2,29]],"date-time":"2016-02-29T20:32:02Z","timestamp":1456777922000},"page":"879-888","source":"Crossref","is-referenced-by-count":2,"title":["RetweetPatterns: Detection of Spatio-Temporal Patterns of Retweets"],"prefix":"10.1007","author":[{"given":"Tomy","family":"Rodrigues","sequence":"first","affiliation":[]},{"given":"Tiago","family":"Cunha","sequence":"additional","affiliation":[]},{"given":"Dino","family":"Ienco","sequence":"additional","affiliation":[]},{"given":"Pascal","family":"Poncelet","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Soares","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,3,2]]},"reference":[{"key":"83_CR1","doi-asserted-by":"crossref","unstructured":"Boanjak, M., Oliveira, E., Martins, J., Mendes Rodrigues, E., Sarmento, L.: TwitterEcho: A Distributed Focused Crawler to Support Open Research with Twitter Data. In: Proceedings of the 21st International Conference Companion on World Wide Web. pp. 1233\u20131240. ACM, New York, NY, USA (2012)","DOI":"10.1145\/2187980.2188266"},{"key":"83_CR2","doi-asserted-by":"crossref","unstructured":"Byun, C., Kim, Y., Lee, H., Kim, K.K.: Automated Twitter Data Collecting Tool and Case Study with Rule-based Analysis. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services. pp. 196\u2013204. IIWAS \u201812, ACM, New York, NY, USA (2012)","DOI":"10.1145\/2428736.2428768"},{"key":"83_CR3","unstructured":"Cunha, T., Soares, C., Mendes Rodrigues, E.: TweeProfiles: dete\u00e7\u00e3o de padr\u00f5es esp\u00e1cio-temporais no Twitter (2013), \n                    http:\/\/hdl.handle.net\/10216\/68545"},{"key":"83_CR4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/978-3-319-14717-8_10","volume-title":"Advanced Data Mining and Applications","author":"Tiago Cunha","year":"2014","unstructured":"Cunha, T., Soares, C., Mendes Rodrigues, E.: TweeProfiles: Detection of Spatio-temporal Patterns on Twitter. In: Advanced Data Mining and Applications, pp. 123\u2013136 (2014)"},{"key":"83_CR5","unstructured":"Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining pp. 226\u2013231 (1996)"},{"key":"83_CR6","unstructured":"Fitzgerald, B.: SGI Twitter Heat Map: Supercomputer Shows Where Angriest Tweeters Live. \n                    http:\/\/www.huffingtonpost.com\/2012\/11\/19\/sgi-twitter-heat-map_n_2138726.html\n                    \n                   (2012)"},{"key":"83_CR7","first-page":"820","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Phan Nhat Hai","year":"2012","unstructured":"Hai, P.N., Ienco, D., Poncelet, P., Teisseire, M.: Extracting Trajectories through an Efficient and Unifying Spatio-temporal Pattern Mining System. In: Flach, P., De Bie, T., Cristianini, N. (eds.) Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, vol. 7524, pp. 820\u2013823. Springer Berlin Heidelberg (2012)"},{"issue":"1-2","key":"83_CR8","doi-asserted-by":"crossref","first-page":"723","DOI":"10.14778\/1920841.1920934","volume":"3","author":"Zhenhui Li","year":"2010","unstructured":"Li, Z., Ding, B., Han, J., Kays, R.: Swarm: Mining Relaxed Temporal Moving Object Clusters. Proc. VLDB Endow. 3(1-2), 723\u2013734 (2010)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"83_CR9","unstructured":"Mentionmapp: Mentionmapp. \n                    http:\/\/mentionmapp.com\/\n                    \n                   (2013)"},{"key":"83_CR10","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/978-3-642-34156-4_26","volume-title":"Advances in Intelligent Data Analysis XI","author":"Phan Nhat Hai","year":"2012","unstructured":"Nhat Hai, P., Poncelet, P., Teisseire, M.: GeT_Move: An Efficient and Unifying Spatio-temporal Pattern Mining Algorithm for Moving Objects. In: Advances in Intelligent Data Analysis XI, vol. 7619, pp. 276\u2013288 (2012)"},{"key":"83_CR11","unstructured":"Richards, J., Lewis, P.: How Twitter was used to spread - and knock down - rumours during the riots. \n                    http:\/\/www.theguardian.com\/uk\/2011\/dec\/07\/how-twitter-spread-rumours-riots\n                    \n                   (2011)"}],"container-title":["Advances in Intelligent Systems and Computing","New Advances in Information Systems and Technologies"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-31232-3_83","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T12:19:32Z","timestamp":1559391572000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-31232-3_83"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319312316","9783319312323"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-31232-3_83","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2016]]}}}