{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:47:17Z","timestamp":1754156837931,"version":"3.41.2"},"reference-count":64,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2022,12,9]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The authors\u2019 proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors\u2019 method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors\u2019 knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-08-2021-0209","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T03:59:22Z","timestamp":1648180762000},"page":"741-761","source":"Crossref","is-referenced-by-count":3,"title":["A collaborative trend prediction method using the crowdsourced wisdom of web search engines"],"prefix":"10.1108","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0806-4773","authenticated-orcid":false,"given":"Ze-Han","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8357-1305","authenticated-orcid":false,"given":"Chien Chin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"issue":"6","key":"key2022120707384866400_ref001","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions","volume":"17","year":"2005","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2022120707384866400_ref002","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.eneco.2017.07.014","article-title":"Google search keywords that best predict energy price volatility","volume":"67","year":"2017","journal-title":"Energy Economics"},{"year":"2010","key":"key2022120707384866400_ref003","article-title":"A survey paper on recommender systems"},{"first-page":"97","article-title":"Trends prediction using social diffusion models","year":"2012","key":"key2022120707384866400_ref004"},{"first-page":"1021","article-title":"The linguistic structure of English web-search queries","year":"2008","key":"key2022120707384866400_ref005"},{"issue":"17","key":"key2022120707384866400_ref006","doi-asserted-by":"crossref","first-page":"6997","DOI":"10.1016\/j.eswa.2013.06.022","article-title":"Advances in clustering collaborative filtering by means of fuzzy C-means and trust","volume":"40","year":"2013","journal-title":"Expert Systems with Applications"},{"key":"key2022120707384866400_ref007","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","year":"2013","journal-title":"Knowledge-based Systems"},{"issue":"10","key":"key2022120707384866400_ref008","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1016\/j.neunet.2004.07.002","article-title":"Fast exact leave-one-out cross-validation of sparse least-squares support vector machines","volume":"17","year":"2004","journal-title":"Neural Networks"},{"key":"key2022120707384866400_ref009","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.knosys.2011.12.012","article-title":"A novel business cycle surveillance system using the query logs of search engines","volume":"30","year":"2012","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"key2022120707384866400_ref010","doi-asserted-by":"crossref","first-page":"112","DOI":"10.35940\/ijitee.A5062.129219","article-title":"Social media aided sentiment analysis for stock prediction","volume":"9","year":"2019","journal-title":"International Journal of Innovative Technology and Exploring Engineering"},{"issue":"2","key":"key2022120707384866400_ref011","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1111\/eufm.12058","article-title":"Can internet search queries help to predict stock market volatility?","volume":"22","year":"2016","journal-title":"European Financial Management"},{"issue":"4","key":"key2022120707384866400_ref012","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1145\/1924421.1924442","article-title":"Crowdsourcing systems on the world-wide web","volume":"54","year":"2011","journal-title":"Communications of the ACM"},{"issue":"05","key":"key2022120707384866400_ref013","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1142\/S0219622019500287","article-title":"Forecasting Chinese stock market prices using Baidu search index with a learning-based data collection method","volume":"18","year":"2019","journal-title":"International Journal of Information Technology and Decision Making"},{"issue":"3","key":"key2022120707384866400_ref014","first-page":"157","article-title":"Forecasting with univariate time series models: a case of export demand for peninsular Malaysia's moulding and chipboard","volume":"3","year":"2010","journal-title":"Journal of Sustainable Development"},{"issue":"4","key":"key2022120707384866400_ref015","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.future.2012.05.026","article-title":"On the performance of high dimensional data clustering and classification algorithms","volume":"29","year":"2013","journal-title":"Future Generation Computer Systems"},{"key":"key2022120707384866400_ref016","first-page":"1438","article-title":"A study of machine learning models in epidemic surveillance: using the query logs of search engines","volume-title":"Proceedings of the 14th Pacific Asia Conference on Information Systems","year":"2010"},{"issue":"7232","key":"key2022120707384866400_ref017","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1038\/nature07634","article-title":"Detecting influenza epidemics using search engine query data","volume":"457","year":"2009","journal-title":"Nature"},{"issue":"12","key":"key2022120707384866400_ref018","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/138859.138867","article-title":"Using collaborative filtering to weave an information tapestry","volume":"35","year":"1992","journal-title":"Communications of the ACM"},{"volume-title":"Search Engine Society","year":"2013","key":"key2022120707384866400_ref019"},{"volume-title":"Applied Statistics for the Behavioral Sciences","year":"2003","key":"key2022120707384866400_ref020"},{"issue":"1","key":"key2022120707384866400_ref021","first-page":"1","article-title":"Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information","volume":"10","year":"2020","journal-title":"Scientific Reports"},{"key":"key2022120707384866400_ref022","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neucom.2018.01.038","article-title":"Predicting the direction of stock markets using optimized neural networks with Google Trends","volume":"285","year":"2018","journal-title":"Neurocomputing"},{"year":"2013","key":"key2022120707384866400_ref023","article-title":"A crowd-powered socially embedded search engine"},{"first-page":"133","article-title":"Optimizing search engines using clickthrough data","year":"2002","key":"key2022120707384866400_ref024"},{"first-page":"165","article-title":"In search of quality in crowdsourcing for search engine evaluation","year":"2011","key":"key2022120707384866400_ref025"},{"issue":"4","key":"key2022120707384866400_ref026","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.dss.2009.05.012","article-title":"Detecting and forecasting economic regimes in multi-agent automated exchanges","volume":"47","year":"2009","journal-title":"Decision Support Systems"},{"article-title":"Crowdsourcing for robustness in web search","volume-title":"Proceedings of NIST Special Publication: The Twenty-Second Text REtrieval Conference","year":"2013","key":"key2022120707384866400_ref027"},{"issue":"1","key":"key2022120707384866400_ref028","first-page":"1","article-title":"Tracking COVID-19 using online search","volume":"4","year":"2021","journal-title":"NPJ Digital Medicine"},{"volume-title":"An Introduction to Recommender Systems","year":"2011","key":"key2022120707384866400_ref029"},{"year":"2015","key":"key2022120707384866400_ref030","article-title":"The cost of asking crowd workers to behave maliciously"},{"year":"2013","key":"key2022120707384866400_ref031","article-title":"Crowdturfers, campaigns, and social media: tracking and revealing crowdsourced manipulation of social media"},{"volume-title":"International and Business Forecasting Methods","year":"1982","key":"key2022120707384866400_ref032"},{"key":"key2022120707384866400_ref033","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.dss.2014.06.007","article-title":"An ontology-based Web mining method for unemployment rate prediction","volume":"66","year":"2014","journal-title":"Decision Support Systems"},{"first-page":"1620","article-title":"Sgas: sequential greedy architecture search","year":"2020","key":"key2022120707384866400_ref034"},{"issue":"6","key":"key2022120707384866400_ref035","doi-asserted-by":"crossref","first-page":"e18181","DOI":"10.2196\/18181","article-title":"Exploring public awareness of overwork prevention with big data from Google trends: retrospective analysis","volume":"22","year":"2020","journal-title":"Journal of Medical Internet Research"},{"issue":"2","key":"key2022120707384866400_ref036","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.dss.2009.02.001","article-title":"Financial time series forecasting using independent component analysis and support vector regression","volume":"47","year":"2009","journal-title":"Decision Support Systems"},{"issue":"1","key":"key2022120707384866400_ref037","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2012.02.006","article-title":"Recommender systems","volume":"519","year":"2012","journal-title":"Physics Reports"},{"volume-title":"Introduction to Information Retrieval","year":"2008","key":"key2022120707384866400_ref038"},{"issue":"1","key":"key2022120707384866400_ref039","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1108\/IJCS-12-2018-0026","article-title":"Crowdsourcing for search engines: perspectives and challenges","volume":"3","year":"2019","journal-title":"International Journal of Crowd Science"},{"issue":"1","key":"key2022120707384866400_ref040","first-page":"87","article-title":"Rogue people: on adversarial crowdsourcing in the context of cyber security","volume":"19","year":"2020","journal-title":"Journal of Information, Communication and Ethics in Society"},{"issue":"3","key":"key2022120707384866400_ref041","first-page":"69","article-title":"A guide to appropriate use of correlation coefficient in medical research","volume":"24","year":"2012","journal-title":"Malawi Medical Journal"},{"issue":"11","key":"key2022120707384866400_ref042","doi-asserted-by":"crossref","first-page":"10059","DOI":"10.1016\/j.eswa.2012.02.038","article-title":"A literature review and classification of recommender systems research","volume":"39","year":"2012","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"key2022120707384866400_ref043","doi-asserted-by":"crossref","first-page":"289","DOI":"10.3743\/KOSIM.2015.32.4.289","article-title":"Information seeking behavior of shopping site users: a log analysis of popshoes, a Korean shopping search engine","volume":"32","year":"2015","journal-title":"Journal of the Korean Society for Information Management"},{"issue":"6","key":"key2022120707384866400_ref044","doi-asserted-by":"crossref","first-page":"102708","DOI":"10.1016\/j.ipm.2021.102708","article-title":"Global cryptocurrency trend prediction using social media","volume":"58","year":"2021","journal-title":"Information Processing and Management"},{"issue":"5","key":"key2022120707384866400_ref045","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1108\/DTA-02-2021-0037","article-title":"Predicting agricultural and livestock products purchases using the Internet search index and data mining techniques","volume":"55","year":"2021","journal-title":"Data Technologies and Applications"},{"issue":"9","key":"key2022120707384866400_ref046","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1080\/13658816.2020.1719495","article-title":"Spatial crime distribution and prediction for sporting events using social media","volume":"34","year":"2020","journal-title":"International Journal of Geographical Information Science"},{"first-page":"955","article-title":"Auditing the personalization and composition of politically-related search engine results pages","year":"2018","key":"key2022120707384866400_ref047"},{"issue":"6","key":"key2022120707384866400_ref048","doi-asserted-by":"crossref","first-page":"494","DOI":"10.2307\/4591848","article-title":"Methods for current statistical analysis of excess pneumonia-influenza deaths","volume":"78","year":"1963","journal-title":"Public Health Reports"},{"key":"key2022120707384866400_ref049","first-page":"242","article-title":"An effective friend recommendation method using learning to rank and social influence","volume-title":"Proceedings of the 19th Pacific Asia Conference on Information Systems","year":"2015"},{"issue":"2","key":"key2022120707384866400_ref050","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1119\/1.2423042","article-title":"The wisdom of crowds","volume":"75","year":"2007","journal-title":"American Journal of Physics"},{"first-page":"1","article-title":"Smart surveillance based on video summarization","year":"2017","key":"key2022120707384866400_ref051"},{"first-page":"2857","article-title":"Seasonal-adjustment based feature selection method for predicting epidemic with large-scale search engine logs","year":"2019","key":"key2022120707384866400_ref052"},{"first-page":"258","article-title":"Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches","year":"2010","key":"key2022120707384866400_ref065"},{"issue":"1","key":"key2022120707384866400_ref053","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-018-9644-0","article-title":"Improving search engine optimization (SEO) by using hybrid modified MCDM models","volume":"53","year":"2020","journal-title":"Artificial Intelligence Review"},{"issue":"3","key":"key2022120707384866400_ref054","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1177\/1354816618811558","article-title":"Forecasting tourist arrivals at attractions: search engine empowered methodologies","volume":"25","year":"2019","journal-title":"Tourism Economics"},{"first-page":"77","article-title":"Crowd-based urban characterization: extracting crowd behavioral patterns in urban areas from twitter","year":"2011","key":"key2022120707384866400_ref055"},{"issue":"6","key":"key2022120707384866400_ref056","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.jvlc.2014.09.011","article-title":"An improved collaborative movie recommendation system using computational intelligence","volume":"25","year":"2014","journal-title":"Journal of Visual Languages"},{"key":"key2022120707384866400_ref057","doi-asserted-by":"crossref","first-page":"113465","DOI":"10.1016\/j.dss.2020.113465","article-title":"Identifying comparable entities with indirectly associative relations and word embeddings from web search logs","volume":"141","year":"2021","journal-title":"Decision Support Systems"},{"issue":"07","key":"key2022120707384866400_ref058","doi-asserted-by":"crossref","first-page":"2150122","DOI":"10.1142\/S021812662150122X","article-title":"Stock turnover prediction using search engine data","volume":"30","year":"2021","journal-title":"Journal of Circuits, Systems and Computers"},{"year":"2020","key":"key2022120707384866400_ref059","article-title":"Time series data augmentation for deep learning: a survey"},{"issue":"4","key":"key2022120707384866400_ref060","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s10796-018-9893-0","article-title":"Social media for nowcasting flu activity: spatio-temporal big data analysis","volume":"21","year":"2019","journal-title":"Information Systems Frontiers"},{"first-page":"84","article-title":"How questions are posed to a search engine? An empiricial analysis of question queries in a large scale Persian search engine log","year":"2017","key":"key2022120707384866400_ref061"},{"key":"key2022120707384866400_ref062","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.jocs.2017.10.013","article-title":"Exploiting investors social network for stock prediction in China's market","volume":"28","year":"2018","journal-title":"Journal of Computational Science"},{"issue":"4","key":"key2022120707384866400_ref063","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1108\/AJIM-10-2015-0165","article-title":"Testing the stability of \u2018wisdom of crowds\u2019 judgments of search results over time and their similarity with the search engine rankings","volume":"68","year":"2016","journal-title":"Aslib Journal of Information Management"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-08-2021-0209\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-08-2021-0209\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:15:18Z","timestamp":1753398918000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/56\/5\/741-761\/48136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":64,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3,28]]},"published-print":{"date-parts":[[2022,12,9]]}},"alternative-id":["10.1108\/DTA-08-2021-0209"],"URL":"https:\/\/doi.org\/10.1108\/dta-08-2021-0209","relation":{},"ISSN":["2514-9288"],"issn-type":[{"type":"print","value":"2514-9288"}],"subject":[],"published":{"date-parts":[[2022,3,28]]}}}