{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:25:03Z","timestamp":1782516303191,"version":"3.54.5"},"reference-count":28,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T00:00:00Z","timestamp":1568592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:p>Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempt visitors to click on a particular link either to monetise the landing page or to spread the false news for sensationalisation. The presence of clickbaits on any news aggregator portal may lead to unpleasant experience to readers. Automatic detection of clickbait headlines from news headlines has been a challenging issue for the machine learning community. A lot of methods have been proposed for preventing clickbait articles in recent past. However, the recent techniques available in detecting clickbaits are not much robust. This article proposes a hybrid categorisation technique for separating clickbait and non-clickbait articles by integrating different features, sentence structure and clustering. During preliminary categorisation, the headlines are separated using 11 features. After that, the headlines are recategorised using sentence formality and syntactic similarity measures. In the last phase, the headlines are again recategorised by applying clustering using word vector similarity based on t-stochastic neighbourhood embedding ( t-SNE) approach. After categorisation of these headlines, machine learning models are applied to the dataset to evaluate machine learning algorithms. The obtained experimental results indicate that the proposed hybrid model is more robust, reliable and efficient than any individual categorisation techniques for the dataset we have used.<\/jats:p>","DOI":"10.1177\/0165551519871822","type":"journal-article","created":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T06:54:36Z","timestamp":1568616876000},"page":"118-128","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":43,"title":["Clickbait detection using multiple categorisation techniques"],"prefix":"10.1177","volume":"47","author":[{"given":"Abinash","family":"Pujahari","sequence":"first","affiliation":[{"name":"Computer Science & Engineering, National Institute of Technology, Raipur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9845-290X","authenticated-orcid":false,"given":"Dilip Singh","family":"Sisodia","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, National Institute of Technology, Raipur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,9,16]]},"reference":[{"key":"bibr1-0165551519871822","first-page":"15","volume-title":"Proceedings of the 2015 ACM on workshop on multimodal deception detection","author":"Chen Y"},{"key":"bibr2-0165551519871822","first-page":"810","volume-title":"European conference on information retrieval","author":"Potthast M"},{"key":"bibr3-0165551519871822","first-page":"84","volume-title":"Proceedings of the 2017 EMNLP workshop: natural language processing meets journalism","author":"Bourgonje P"},{"key":"bibr4-0165551519871822","unstructured":"Hurst N.\n                      To clickbait or not to clickbait? An\n                      examination of clickbait headline effects on source credibility. Master\u2019s Thesis, University of Missouri, Columbia, MO, 2016."},{"key":"bibr5-0165551519871822","first-page":"268","volume-title":"2016 2nd international conference on next generation computing technologies (NGCT)","author":"Agrawal A."},{"key":"bibr6-0165551519871822","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63751-8_5"},{"key":"bibr7-0165551519871822","first-page":"63","volume-title":"IEEE symposium on security and privacy workshops (SPW 2018)","author":"Zannettou S"},{"key":"bibr8-0165551519871822","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-017-1109-7"},{"key":"bibr9-0165551519871822","first-page":"1225","volume-title":"proceedings of the 41st international ACM SIGIR conference on research & development in information retrieval","author":"Kumar V"},{"key":"bibr10-0165551519871822","first-page":"38","volume":"1999","author":"Heylighen F","year":"1999","journal-title":"Interner Bericht"},{"key":"bibr11-0165551519871822","doi-asserted-by":"publisher","DOI":"10.3758\/BF03195564"},{"key":"bibr12-0165551519871822","unstructured":"Finnis A. List of bad words. BuzzFeed, 29 October 2015, https:\/\/www.buzzfeed.com\/alexfinnis\/the-100-most-brilliantly-british-swear-words-in-existence"},{"key":"bibr13-0165551519871822","first-page":"9","volume-title":"Proceedings of 2016 IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM)","author":"Chakraborty A"},{"key":"bibr14-0165551519871822","first-page":"55","volume-title":"Proceedings of 52nd annual meeting of the Association for Computational Linguistics: system demonstration","author":"Manning C"},{"key":"bibr15-0165551519871822","first-page":"94","volume-title":"Proceedings of the 30th AAAI conference on artificial intelligence (AAAI-16)","author":"Biyani P"},{"key":"bibr16-0165551519871822","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"bibr17-0165551519871822","first-page":"232","volume-title":"Proceedings of the 2017 IEEE\/ACM international conference on advances in social networks analysis and mining 2017","author":"Rony MMU"},{"key":"bibr18-0165551519871822","unstructured":"Bojanowski P, Grave E, Joulin A et al. Enriching word vectors with subword information, 2016, https:\/\/arxiv.org\/abs\/1607.04606"},{"key":"bibr19-0165551519871822","doi-asserted-by":"publisher","DOI":"10.21236\/ADA006655"},{"key":"bibr20-0165551519871822","unstructured":"Mikolov T, Chen K, Corrado G et al. Efficient estimation of word representations in vector space, 2013, https:\/\/arxiv.org\/abs\/1301.3781"},{"key":"bibr21-0165551519871822","first-page":"2579","volume":"9","author":"Van Der Maaten LJP","year":"2008","journal-title":"J Mach Learn Res"},{"key":"bibr22-0165551519871822","first-page":"857","volume-title":"Advances in neural information processing systems","author":"Hinton GE","year":"2003"},{"key":"bibr23-0165551519871822","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"bibr24-0165551519871822","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-08-050058-4.50007-3"},{"issue":"5","key":"bibr25-0165551519871822","first-page":"1","volume":"45","author":"Breiman L.","year":"1999","journal-title":"Mach Learn"},{"key":"bibr26-0165551519871822","doi-asserted-by":"publisher","DOI":"10.14445\/22312803\/IJCTT-V10P107"},{"key":"bibr27-0165551519871822","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1"},{"key":"bibr28-0165551519871822","first-page":"625","volume-title":"Proceedings of the 22nd international conference on machine learning","author":"Niculescu-Mizil A"}],"container-title":["Journal of Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551519871822","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0165551519871822","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551519871822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:08:59Z","timestamp":1777504139000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0165551519871822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,16]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["10.1177\/0165551519871822"],"URL":"https:\/\/doi.org\/10.1177\/0165551519871822","relation":{},"ISSN":["0165-5515","1741-6485"],"issn-type":[{"value":"0165-5515","type":"print"},{"value":"1741-6485","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,16]]}}}