{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:45:20Z","timestamp":1775245520269,"version":"3.50.1"},"reference-count":259,"publisher":"Emerald","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,5]]},"abstract":"<jats:p>The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users\u2019 interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users\u2019 interests and preferences. In this monograph, we will cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and opportunities for further work.<\/jats:p>","DOI":"10.1561\/1500000078","type":"journal-article","created":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T09:13:39Z","timestamp":1604567619000},"page":"445-617","source":"Crossref","is-referenced-by-count":6,"title":["Extracting, Mining and Predicting Users\u2019 Interests from Social Media"],"prefix":"10.1561","volume":"14","author":[{"given":"Fattane","family":"Zarrinkalam","sequence":"first","affiliation":[{"name":"Ryerson University ,","place":["Canada"]}]},{"given":"Stefano","family":"Faralli","sequence":"additional","affiliation":[{"name":"University of Rome Unitelma Sapienza ,","place":["Italy"]}]},{"given":"Guangyuan","family":"Piao","sequence":"additional","affiliation":[{"name":"Insight Centre for Data Analytics, NUI Galway ,","place":["Ireland"]}]},{"given":"Ebrahim","family":"Bagheri","sequence":"additional","affiliation":[{"name":"Ryerson University ,","place":["Canada"]}]}],"member":"140","published-online":{"date-parts":[[2020,11,5]]},"reference":[{"key":"2026040314425693900_ref001","first-page":"831","article-title":"A cloud based framework for identification of influential health experts from Twitter","volume-title":"2015 IEEE 12th Inti. 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