{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:30:45Z","timestamp":1760596245169,"version":"3.41.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2015,6,2]],"date-time":"2015-06-02T00:00:00Z","timestamp":1433203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Project 2012CB316304, in part by the National Natural Science Foundation of China","award":["61201374, Grant 61432019, and Grant 61225009"],"award-info":[{"award-number":["61201374, Grant 61432019, and Grant 61225009"]}]},{"name":"National Program on Key Basic Research Project 973 Program"},{"name":"Beijing Natural Science Foundation (4152053 and 4131004)"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2015,6,2]]},"abstract":"<jats:p>With the explosive growth of online media platforms in recent years, it becomes more and more attractive to provide users a solution of emerging topic detection and elaboration. And this posts a real challenge to both industrial and academic researchers because of the overwhelming information available in multiple modalities and with large outlier noises. This article provides a method on emerging topic detection and elaboration using multimedia streams cross different online platforms. Specifically, Twitter, New York Times and Flickr are selected for the work to represent the microblog, news portal and imaging sharing platforms. The emerging keywords of Twitter are firstly extracted using aging theory. Then, to overcome the nature of short length message in microblog, Robust Cross-Platform Multimedia Co-Clustering (RCPMM-CC) is proposed to detect emerging topics with three novelties: 1) The data from different media platforms are in multimodalities; 2) The coclustering is processed based on a pairwise correlated structure, in which the involved three media platforms are pairwise dependent; 3) The noninformative samples are automatically pruned away at the same time of coclustering. In the last step of cross-platform elaboration, we enrich each emerging topic with the samples from New York Times and Flickr by computing the implicit links between social topics and samples from selected news and Flickr image clusters, which are obtained by RCPMM-CC. Qualitative and quantitative evaluation results demonstrate the effectiveness of our method.<\/jats:p>","DOI":"10.1145\/2730889","type":"journal-article","created":{"date-parts":[[2015,6,2]],"date-time":"2015-06-02T18:19:47Z","timestamp":1433269187000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Cross-Platform Emerging Topic Detection and Elaboration from Multimedia Streams"],"prefix":"10.1145","volume":"11","author":[{"given":"Bing-Kun","family":"Bao","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"}]},{"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"}]},{"given":"Weiqing","family":"Min","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"}]},{"given":"Mohammod Shamim","family":"Hossain","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, King Saud University"}]}],"member":"320","published-online":{"date-parts":[[2015,6,2]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2013.2265080"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989473"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1314498.1314563"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2393347.2396482"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2317514"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461466.2461491"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2393347.2396483"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2008.921739"},{"volume-title":"Proceedings of the International Conference on Machine Learning. 47--59","author":"Chen C.","key":"e_1_2_2_9_1","unstructured":"C. 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