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Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A case study was conducted to analyze nearly one million Twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl\u2019s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>This case study confirms that this proposed framework is sensible and useful in terms of integrating Big Data technology, social media and KM in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>Practical guidance for integrating Big Data, social media and KM is scarce. This proposed framework is a pioneering effort in using Big Data technologies to extract valuable knowledge on social media and discover business intelligence by contextually comparing social media knowledge among competitors.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/jkm-07-2015-0296","type":"journal-article","created":{"date-parts":[[2017,4,19]],"date-time":"2017-04-19T09:21:13Z","timestamp":1492593673000},"page":"275-294","source":"Crossref","is-referenced-by-count":107,"title":["Managing extracted knowledge from big social media data for business decision making"],"prefix":"10.1108","volume":"21","author":[{"given":"Wu","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng-Kwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasudeva","family":"Akula","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"1","key":"key2020120808103123600_ref001","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1111\/j.1467-8535.2009.00980.x","article-title":"Using text mining to uncover students\u2019 technology-related problems in live video streaming","volume":"42","year":"2011","journal-title":"British Journal of Educational Technology"},{"key":"key2020120808103123600_ref002","volume-title":"Mining Text Data","year":"2012"},{"issue":"1","key":"key2020120808103123600_ref003","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1108\/VINE-08-2012-0034","article-title":"Knowledge management critical failure factors: a multi-case study","volume":"44","year":"2014","journal-title":"VINE: The Journal of Information and Knowledge Management Systems"},{"issue":"1","key":"key2020120808103123600_ref004","doi-asserted-by":"crossref","first-page":"107","DOI":"10.2307\/3250961","article-title":"Knowledge management and knowledge management systems: conceptual foundations and research issues","volume":"25","year":"2001","journal-title":"MIS Quarterly"},{"key":"key2020120808103123600_ref005","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/978-1-4419-8462-3_12","article-title":"Data mining in social media","volume-title":"Social Network Data Analytics","year":"2011"},{"issue":"3","key":"key2020120808103123600_ref006","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1108\/JKM-11-2014-0467","article-title":"Better knowledge with social media? 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