{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:34:32Z","timestamp":1754156072038,"version":"3.41.2"},"reference-count":37,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2016,8,15]],"date-time":"2016-08-15T00:00:00Z","timestamp":1471219200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2016,8,15]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Identifying important users from social media has recently attracted much attention in the information and knowledge management community. Although researchers have focused on users\u2019 knowledge levels on certain topics or influence degrees on other users in social networks, previous works have not studied users\u2019 prediction ability on future popularity. This paper aims to propose a novel approach to find prophetic bloggers based on their buzzword prediction ability.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The main approach is to conduct a time-series analysis in the blogosphere considering four factors: post earliness, content similarity, entry frequency and buzzword coverage. Our method has four steps: categorizing a blogger into knowledgeable categories, identifying past buzzwords, analyzing a buzzword\u2019s peak time content and growth period and, finally, evaluating a blogger\u2019s prediction ability on a buzzword and on a category.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Experimental results on real-world blog data consisting of 150 million entries from 11 million bloggers demonstrate that the proposed approach can find prophetic bloggers and outperforms others that do not take temporal features into account.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>To the best of the authors\u2019 knowledge, our approach is the first successful attempt to identify prophetic bloggers. Finding prophetic bloggers can bring great values for two reasons. First, as prophetic bloggers tend to post creative and insightful information, analysis on their blog entries may help find future buzzword candidates. Second, communication with prophetic bloggers can help understand future trends, gain insight into early adopters\u2019 thoughts on new technology or even foresee things that will become popular.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-03-2016-0013","type":"journal-article","created":{"date-parts":[[2016,8,16]],"date-time":"2016-08-16T05:38:07Z","timestamp":1471325887000},"page":"267-291","source":"Crossref","is-referenced-by-count":2,"title":["Prophetic blogger identification based on buzzword prediction ability"],"prefix":"10.1108","volume":"12","author":[{"given":"Jianwei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seiya","family":"Tomonaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shinsuke","family":"Nakajima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoichi","family":"Inagaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reyn","family":"Nakamoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"first-page":"207","article-title":"Identifying the influential bloggers in a community","year":"2008","key":"key2020121321385761700_ref001"},{"first-page":"607","article-title":"A peek into the future: predicting the evolution of popularity in user generated content","year":"2013","key":"key2020121321385761700_ref002"},{"year":"2011","key":"key2020121321385761700_ref003","article-title":"Trends in social media: persistence and decay"},{"first-page":"65","article-title":"Everyone\u2019s an influencer: quantifying influence on twitter","year":"2011","key":"key2020121321385761700_ref004"},{"issue":"2\/3","key":"key2020121321385761700_ref006","first-page":"127","article-title":"Expertise retrieval","volume":"6","year":"2012","journal-title":"Foundations and Trends in Information Retrieval"},{"first-page":"753","article-title":"Bloggers as experts: feed distillation using expert retrieval models","year":"2008","key":"key2020121321385761700_ref005"},{"year":"2012","key":"key2020121321385761700_ref007","article-title":"The pulse of news in social media: forecasting popularity"},{"year":"2011","key":"key2020121321385761700_ref008","article-title":"Beyond trending topics: real-world event identification on twitter"},{"first-page":"537","article-title":"Predicting trending messages and diffusion participants in microblogging network","year":"2014","key":"key2020121321385761700_ref009"},{"first-page":"637","article-title":"Choosing the right crowd: expert finding in social networks","year":"2013","key":"key2020121321385761700_ref010"},{"first-page":"1109","article-title":"Mass: a multi-facet domain-specific influential blogger mining system","year":"2010","key":"key2020121321385761700_ref011"},{"year":"2010","key":"key2020121321385761700_ref012","article-title":"Measuring user influence in twitter: the million follower fallacy"},{"issue":"1","key":"key2020121321385761700_ref013","first-page":"22","article-title":"Word association norms, mutual information, and lexicography","volume":"16","year":"1990","journal-title":"Computational Linguistics"},{"first-page":"745","article-title":"The tube over time: characterizing popularity growth of youtube videos","year":"2011","key":"key2020121321385761700_ref014"},{"first-page":"295","article-title":"Expediting search trend detection via prediction of query counts","year":"2013","key":"key2020121321385761700_ref015"},{"first-page":"499","article-title":"Discovering leaders from community actions","year":"2008","key":"key2020121321385761700_ref016"},{"edition":"2nd ed.","volume-title":"Nonparametric Statistics: A Step-by-Step Approach","year":"2014","key":"key2020121321385761700_ref017"},{"first-page":"515","article-title":"Mining expertise and interests from social media","year":"2013","key":"key2020121321385761700_ref018"},{"first-page":"1117","article-title":"Expertise retrieval in bibliographic network: a topic dominance learning approach","year":"2013","key":"key2020121321385761700_ref019"},{"first-page":"57","article-title":"Predicting popular messages in twitter","year":"2011","key":"key2020121321385761700_ref020"},{"first-page":"481","article-title":"Topic initiator detection on the world wide web","year":"2010","key":"key2020121321385761700_ref021"},{"year":"2013","key":"key2020121321385761700_ref022","article-title":"Towards supporting search over trending events with social media"},{"key":"key2020121321385761700_ref023","first-page":"125","article-title":"Sketching words","volume-title":"Lexicography and Natural Language Processing: A Festschrift in Honour of B. 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