{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T02:27:38Z","timestamp":1772764058975,"version":"3.50.1"},"reference-count":82,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2018,3,6]],"date-time":"2018-03-06T00:00:00Z","timestamp":1520294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JKM"],"published-print":{"date-parts":[[2018,6,15]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users\u2019 domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users\u2019 historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users\u2019 tweets.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>This study is limited to an on\/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter\u2019s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer\/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users\u2019 domains of interest.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/jkm-11-2016-0489","type":"journal-article","created":{"date-parts":[[2018,3,6]],"date-time":"2018-03-06T05:24:13Z","timestamp":1520313853000},"page":"949-981","source":"Crossref","is-referenced-by-count":62,"title":["Twitter mining for ontology-based domain discovery incorporating machine learning"],"prefix":"10.1108","volume":"22","author":[{"given":"Bilal","family":"Abu-Salih","sequence":"first","affiliation":[]},{"given":"Pornpit","family":"Wongthongtham","sequence":"additional","affiliation":[]},{"given":"Chan","family":"Yan Kit","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2018,3,6]]},"reference":[{"key":"key2021041509193787800_ref001","first-page":"460","article-title":"A preliminary approach to domain-based evaluation of users\u2019 trustworthiness in online social networks","year":"2015"},{"key":"key2021041509193787800_ref002","article-title":"Towards a methodology for social business intelligence in the era of big social data incorporating trust and semantic analysis","year":"2015"},{"key":"key2021041509193787800_ref003","first-page":"1","article-title":"Hashtag-based topic evolution in social media","year":"2017"},{"key":"key2021041509193787800_ref004","article-title":"A Survey of Topic Modeling in Text Mining","year":"2015"},{"key":"key2021041509193787800_ref005","first-page":"71","article-title":"Arabic text categorization using logistic regression","volume-title":"International Journal of Intelligent Systems and Applications","year":"2015"},{"key":"key2021041509193787800_ref006","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.engappai.2015.03.015","article-title":"A corpus-based semantic kernel for text classification by using meaning values of terms","volume":"43","year":"2015","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"12","key":"key2021041509193787800_ref007","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/1859204.1859210","article-title":"Topic models vs. unstructured data","volume":"53","year":"2010","journal-title":"Communications of the ACM"},{"issue":"1","key":"key2021041509193787800_ref008","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1108\/13673270610650139","article-title":"Cultural influences on knowledge sharing through online communities of practice","volume":"10","year":"2006","journal-title":"Journal of knowledge management"},{"key":"key2021041509193787800_ref009","first-page":"824","article-title":"Generating and visualizing topic hierarchies from microblogs: an iterative latent dirichlet allocation approach","year":"2015"},{"key":"key2021041509193787800_ref010","unstructured":"BBC. 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