{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:34:48Z","timestamp":1754156088651,"version":"3.41.2"},"reference-count":31,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2013,8,23]],"date-time":"2013-08-23T00:00:00Z","timestamp":1377216000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,8,23]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule\u2010based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule\u2010based analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The authors developed a java\u2010based data gathering tool with a rule\u2010based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study shows that a historical event creates buzz on Twitter and people's interests on the event are reflected in their Twitter activity.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>Applying data\u2010mining techniques to the social network data has so much potential. A possible improvement to the Twitter Data Collecting Tool would be an adaptation of a built\u2010in data\u2010mining module.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>This paper focuses on designing a system handling massive amounts of Twitter Data. This is the first approach to embed a rule engine for filtering and analyzing social data. This paper will be valuable to those who may want to build their own Twitter dataset, apply customized filtering options to get rid of unnecessary, noisy data, and analyze social data to discover new knowledge.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijwis-04-2013-0011","type":"journal-article","created":{"date-parts":[[2013,8,14]],"date-time":"2013-08-14T12:58:55Z","timestamp":1376485135000},"page":"184-203","source":"Crossref","is-referenced-by-count":5,"title":["Twitter data collecting tool with rule\u2010based filtering and analysis module"],"prefix":"10.1108","volume":"9","author":[{"given":"Changhyun","family":"Byun","sequence":"first","affiliation":[]},{"given":"Hyeoncheol","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Yanggon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Kwangmi","family":"Ko Kim","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022031620143655700_b1","doi-asserted-by":"crossref","unstructured":"Al\u2010Khalifa, H. 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(2011), \u201cDesign and implementation of the context\u2010aware collaboration framework with the XCREAM (XLogic collaborative RFID\/USN\u2010enabled adaptive middleware)\u201d, paper presented at The Third International Conference on Smart IT Applications."},{"key":"key2022031620143655700_b29","doi-asserted-by":"crossref","unstructured":"Choi, Y. and Cardie, C. (2009), \u201cAdapting a polarity lexicon using integer linear programming for domain\u2010specific sentiment classification\u201d, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, Association for Computational Linguistics, Stroudsburg, PA, pp. 590\u2010598.","DOI":"10.3115\/1699571.1699590"},{"key":"key2022031620143655700_b6","doi-asserted-by":"crossref","unstructured":"Correa, D. and Sureka, A. 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