{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T16:54:34Z","timestamp":1710953674509},"reference-count":20,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,1]]},"abstract":"

The new communication paradigm established by social media along with its growing popularity in recent years contributed to attract an increasing interest of several research fields. One such research field is the field of event detection in social media. The contribution of this article is to implement a system to detect newsworthy events in Twitter. The proposed pipeline first splits the tweets into segments. These segments are then ranked. The top k segments in this ranking are then grouped together. Finally, the resulting candidate events are filtered in order to retain only those related to real-world newsworthy events. The implemented system was tested with three months of data, representing a total of 4,770,636 tweets written in Portuguese. In terms of performance, the proposed approach achieved an overall precision of 88% and a recall of 38%.<\/p>","DOI":"10.4018\/ijoci.2019070103","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T18:48:51Z","timestamp":1556563731000},"page":"45-62","source":"Crossref","is-referenced-by-count":1,"title":["Framework for the Discovery of Newsworthy Events in Social Media"],"prefix":"10.4018","volume":"9","author":[{"given":"Fernando Jos\u00e9 Fradique","family":"Duarte","sequence":"first","affiliation":[{"name":"University of Aveiro, Aveiro, Portugal"}]},{"given":"\u00d3scar Mort\u00e1gua","family":"Pereira","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, DETI \u2013 University of Aveiro, Aveiro, Portugal"}]},{"given":"Rui L.","family":"Aguiar","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, DETI \u2013 University of Aveiro, Aveiro, Portugal"}]}],"member":"2432","reference":[{"key":"IJOCI.2019070103-0","doi-asserted-by":"publisher","DOI":"10.1145\/2996183"},{"key":"IJOCI.2019070103-1","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12017"},{"key":"IJOCI.2019070103-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2017.11.002"},{"key":"IJOCI.2019070103-3","first-page":"785","article-title":"XGBoost\u202f: A Scalable Tree Boosting System.","author":"T.Chen","year":"2016","journal-title":"Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining"},{"key":"IJOCI.2019070103-4","unstructured":"da Silva, J. F., & Lopes, G. P. (1999). A Local Maxima method and a Fair Dispersion Normalization for extracting multi-word units from corpora. In Proceedings of the 6th Meeting on the Mathematics of Language (pp. 369\u2013381). Academic Press."},{"key":"IJOCI.2019070103-5","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1973.223640"},{"key":"IJOCI.2019070103-6","doi-asserted-by":"publisher","DOI":"10.1145\/1348549.1348556"},{"key":"IJOCI.2019070103-7","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2396785"},{"key":"IJOCI.2019070103-8","doi-asserted-by":"publisher","DOI":"10.1145\/2348283.2348380"},{"key":"IJOCI.2019070103-9","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.125"},{"key":"IJOCI.2019070103-10","unstructured":"Madani, A., Boussaid, O., & Zegour, D. E. (2014). What\u2019 s Happening: A Survey of Tweets Event Detection. In Proceedings of the Third International Conference on Communications, Computation, Networks and Technologies (pp. 16\u201322). Nice, France: Academic Press."},{"key":"IJOCI.2019070103-11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41706-6_2"},{"key":"IJOCI.2019070103-12","article-title":"SNOW 2014 Data Challenge: Assessing the Performance of News Topic Detection Methods in Social Media.","author":"S.Papadopoulos","year":"2014","journal-title":"Proceedings of the SNOW 2014 Data Challenge"},{"key":"IJOCI.2019070103-13","doi-asserted-by":"publisher","DOI":"10.1007\/s13398-014-0173-7.2"},{"key":"IJOCI.2019070103-14","doi-asserted-by":"publisher","DOI":"10.1109\/WI-IAT.2010.205"},{"key":"IJOCI.2019070103-15","doi-asserted-by":"publisher","DOI":"10.1145\/1963192.1963246"},{"key":"IJOCI.2019070103-16","unstructured":"Qin, Y., Zhang, Y., Zhang, M., & Zheng, D. (2013). Feature-Rich Segment-Based News Event Detection on Twitter. In Sixth International Joint Conference on Natural Language Processing (pp. 302\u2013310). Nagoya, Japan: Asian Federation of Natural Language Processing."},{"key":"IJOCI.2019070103-17","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772777"},{"key":"IJOCI.2019070103-18","first-page":"25","article-title":"Detecting Newsworthy Topics in Twitter.","author":"S.Van Canneyt","year":"2014","journal-title":"Proceedings of the SNOW 2014 Data Challenge"},{"key":"IJOCI.2019070103-19","article-title":"TVPulse: detecting TV highlights in Social Networks.","author":"A.Vila\u00e7a","year":"2015","journal-title":"10th Conference on Telecommunications"}],"container-title":["International Journal of Organizational and Collective Intelligence"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=228203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T16:15:05Z","timestamp":1651853705000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJOCI.2019070103"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,7,1]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,7]]}},"URL":"https:\/\/doi.org\/10.4018\/ijoci.2019070103","relation":{},"ISSN":["1947-9344","1947-9352"],"issn-type":[{"value":"1947-9344","type":"print"},{"value":"1947-9352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,1]]}}}