{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:28:38Z","timestamp":1777854518175,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"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":[[2023,4]]},"abstract":"<jats:p>Clickbait is a strategy that aims to attract people\u2019s attention and direct them to specific content. Clickbait titles, created by the information that is not included in the main content or using intriguing expressions with various text-related features, have become very popular, especially in social media. This study expands the Turkish clickbait dataset that we had constructed for clickbait detection in our proof-of-concept study, written in Turkish. We achieve a 48,060 sample size by adding 8859 tweets and release a publicly available dataset \u2013 ClickbaitTR \u2013 with its open-source data analysis library. We apply machine learning algorithms such as Artificial Neural Network (ANN), Logistic Regression, Random Forest, Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Ensemble Classifier on 48,060 news headlines extracted from Twitter. The results show that the Logistic Regression algorithm has 85% accuracy; the Random Forest algorithm has a performance of 86% accuracy; the LSTM has 93% accuracy; the ANN has 93% accuracy; the Ensemble Classifier has 93% accuracy; and finally, the BiLSTM has 97% accuracy. A thorough discussion is provided for the psychological aspects of clickbait strategy focusing on curiosity and interest arousal. In addition to a successful clickbait detection performance and the detailed analysis of clickbait sentences in terms of language and psychological aspects, this study also contributes to clickbait detection studies with the largest clickbait dataset in Turkish.<\/jats:p>","DOI":"10.1177\/01655515211007746","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:27:01Z","timestamp":1618273621000},"page":"480-499","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["ClickbaitTR: Dataset for clickbait detection from Turkish news sites and social media with a comparative analysis via machine learning algorithms"],"prefix":"10.1177","volume":"49","author":[{"given":"\u015eura","family":"Gen\u00e7","sequence":"first","affiliation":[{"name":"Department of Cognitive Science, Graduate School of Informatics, Middle East Technical University, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0738-6669","authenticated-orcid":false,"given":"Elif","family":"Surer","sequence":"additional","affiliation":[{"name":"Department of Modeling and Simulation, Graduate School of Informatics, Middle East Technical University, Turkey"}]}],"member":"179","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"1498","volume-title":"Proceedings of the 27th international conference on computational linguistics","author":"Potthast M","unstructured":"Potthast M, Gollub T, Komlossy K, et al. Crowdsourcing a large corpus of clickbait on twitter. In: Proceedings of the 27th international conference on computational linguistics, Santa Fe, NM, 20\u201326 August 2018, pp. 1498\u20131507. New York: ACL."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1037\/0033-2909.116.1.75"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1037\/11164-000"},{"key":"e_1_3_3_5_2","first-page":"94","volume-title":"Proceedings of the thirtieth AAAI conference on artificial intelligence","author":"Biyani P","unstructured":"Biyani P, Tsioutsiouliklis K, Blackmer J. \u20188 amazing secrets for getting more clicks\u2019: Detecting clickbaits in news streams using article informality. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, Phoenix, AZ, 21 February 2016, pp. 94\u2013100. Reston, VA: AAAI."},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551519871822"},{"key":"e_1_3_3_7_2","unstructured":"Platform GNBD. Statista n.d. http:\/\/www.statista.com (accessed July 2019)."},{"key":"e_1_3_3_8_2","first-page":"65","volume-title":"Proceedings of the fourth ACM international conference on web search and data mining","author":"Bakshy E","unstructured":"Bakshy E, Hofman JM, Mason WA, et al. Everyone\u2019s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on web search and data mining, Hong Kong, China, 9\u201312 February 2011, pp. 65\u201374. New York: ACM."},{"key":"e_1_3_3_9_2","first-page":"932","volume-title":"Proceedings of the 2018 IEEE\/ACM international conference on advances in social networks analysis and mining","author":"Ge\u00e7kil A","unstructured":"Ge\u00e7kil A, M\u00fcngen AA, G\u00fcndogan E, et al. A clickbait detection method on news sites. In: Proceedings of the 2018 IEEE\/ACM international conference on advances in social networks analysis and mining, Barcelona, 28\u201331 August 2018, pp. 932\u2013937. New York: IEEE."},{"key":"e_1_3_3_10_2","unstructured":"Alexa. The top 500 sites on the web n.d. https:\/\/www.alexa.com\/topsites (accessed July 2019)."},{"key":"e_1_3_3_11_2","first-page":"1","volume-title":"Proceedings of the fourth workshop on building and evaluating resources for health and biomedical text processing","author":"Ginn R","unstructured":"Ginn R, Pimpalkhute P, Nikfarjam A, et al. Mining Twitter for adverse drug reaction mentions: a corpus and classification benchmark. In: Proceedings of the fourth workshop on building and evaluating resources for health and biomedical text processing, Reykjavik, 31 May 2014, pp. 1\u20138. CiteSeerx."},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.07.279"},{"key":"e_1_3_3_13_2","first-page":"810","volume-title":"European Conference on Information Retrieval","author":"Potthast M","unstructured":"Potthast M, K\u00f6psel S, Stein B, et al. Clickbait detection. In: European Conference on Information Retrieval, Padua, 20\u201323 March 2016, pp. 810\u2013817. Cham: Springer."},{"key":"e_1_3_3_14_2","first-page":"375","volume-title":"Proceedings of the 18th ACM conference on information and knowledge management","author":"Lin C","unstructured":"Lin C, He Y. Joint sentiment\/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on information and knowledge management, Hong Kong, China, 2\u20136 November 2009, pp. 375\u2013384. New York: ACM."},{"key":"e_1_3_3_15_2","first-page":"1","volume-title":"Proceedings of the 1st international workshop on emotion and sentiment in social and expressive media: approaches and perspectives from AI","author":"Saif H","unstructured":"Saif H, Fernandez M, He Y, et al. Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. In: Proceedings of the 1st international workshop on emotion and sentiment in social and expressive media: approaches and perspectives from AI, Turin, 3 December 2013, pp. 1\u201313.CEUR-WS."},{"key":"e_1_3_3_16_2","first-page":"2734","volume-title":"Proceedings of the 2017 IEEE international conference on power, control, signals and instrumentation engineering","author":"Sisodia DS","unstructured":"Sisodia DS, Reddy NR. Sentiment analysis of prospective buyers of mega online sale using tweets. In: Proceedings of the 2017 IEEE international conference on power, control, signals and instrumentation engineering, Chennai, India, 21\u201322 September 2017, pp. 2734\u20132739. New York: IEEE."},{"key":"e_1_3_3_17_2","first-page":"915","volume-title":"Proceedings of the 2017 IEEE\/ACM international conference on advances in social networks analysis and mining","author":"Hsu D","unstructured":"Hsu D, Moh M, Moh TS. Mining frequency of drug side effects over a large twitter dataset using apache spark. In: Proceedings of the 2017 IEEE\/ACM international conference on advances in social networks analysis and mining, Sydney, NSW, Australia, 31 July\u20133 August 2017, pp. 915\u2013924. New York: IEEE."},{"key":"e_1_3_3_18_2","first-page":"702","volume-title":"Proceedings of the 2011 IEEE conference on computer communications workshops","author":"Achrekar H","unstructured":"Achrekar H, Gandhe A, Lazarus R, et al. Predicting flu trends using twitter data. In: Proceedings of the 2011 IEEE conference on computer communications workshops, Shanghai, China, 10\u201315 April 2011, pp. 702\u2013707. New York: IEEE."},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19071746"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi6100302"},{"key":"e_1_3_3_21_2","unstructured":"Zhou Y. Clickbait detection in tweets using self-attentive network 2017 https:\/\/arxiv.org\/abs\/1710.05364."},{"key":"e_1_3_3_22_2","unstructured":"Qu J Hi\u00dfbach AM Gollub T et al. Towards crowdsourcing clickbait labels for YouTube videos. In: Proceedings of the HCOMP 2018 works in progress and demonstration papers Z\u00fcrich 5\u20138 July 2018 pp. 1\u20134. CEUR-WS."},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.07.022"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-017-1109-7"},{"key":"e_1_3_3_25_2","doi-asserted-by":"crossref","unstructured":"Chakraborty A Sarkar R Mrigen A et al. Tabloids in the era of social media? Understanding the production and consumption of clickbaits in twitter. In: Proceedings of the ACM on human-computer interaction Portland OR 6 December 2017 pp. 1\u201321. New York: ACM.","DOI":"10.1145\/3134665"},{"key":"e_1_3_3_26_2","unstructured":"Bhowmik S Rony MM Haque MM et al. Examining the Role of Clickbait Headlines to Engage Readers with Reliable Health-related Information 2019 https:\/\/arxiv.org\/abs\/1911.11214"},{"key":"e_1_3_3_27_2","unstructured":"Tirajlar\u0131 G. Weekly Circulations n.d. http:\/\/gazetetirajlari.com (accessed August 2020)."},{"key":"e_1_3_3_28_2","first-page":"1","volume-title":"Proceedings of the 2019 27th signal processing and communications applications conference (SIU)","author":"Gen\u00e7\u015e and Surer E","unstructured":"Gen\u00e7\u015e and Surer E. Detecting \u2018clickbait\u2019 news on social media using machine learning algorithms. In: Proceedings of the 2019 27th signal processing and communications applications conference (SIU), Sivas, 24\u201326 April 2019, pp. 1\u20134. IEEE."},{"key":"e_1_3_3_29_2","unstructured":"@LimonHaber. Limon Haber n.d. https:\/\/twitter.com\/LimonHaber (accessed August 2020)."},{"key":"e_1_3_3_30_2","unstructured":"@spoilerhaber. Spoiler Haber n.d. https:\/\/twitter.com\/spoilerhaber (accessed August 2020)."},{"key":"e_1_3_3_31_2","unstructured":"@evrenselgzt. Evrensel Newspaper n.d. https:\/\/twitter.com\/evrenselgzt (accessed August 2020)."},{"key":"e_1_3_3_32_2","unstructured":"@DikenComTr. Diken Newspaper n.d. https:\/\/twitter.com\/DikenComTr (accessed August 2020)."},{"key":"e_1_3_3_33_2","unstructured":"Radar M. Circulation Report for the week of 17 August\u201323 August n.d. https:\/\/www.medyaradar.com\/tirajlar (accessed August 2020)."},{"key":"e_1_3_3_34_2","unstructured":"API T. Use Cases Tutorials Documentation Twitter Developer n.d. https:\/\/developer.twitter.com (accessed July 2019)."},{"key":"e_1_3_3_35_2","unstructured":"Tweepy. An easy-to-use Python library for accessing the Twitter API n.d. https:\/\/www.tweepy.org (accessed July 2019)."},{"key":"e_1_3_3_36_2","first-page":"45","volume-title":"Proceedings of the LREC 2010 workshop on new challenges for NLP Frameworks","author":"Rehurek R","unstructured":"Rehurek R, Sojka P. Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP Frameworks, Valletta, 22 May 2010, pp. 45\u201350. University of Malta."},{"key":"e_1_3_3_37_2","volume-title":"Linguistics: An introduction to language and communication","author":"Akmajian A","year":"2017","unstructured":"Akmajian A, Farmer AK, Bickmore L, et al. Linguistics: An introduction to language and communication. Cambridge, MA: MIT Press, 2017."},{"key":"e_1_3_3_38_2","unstructured":"Aksoy A. Kalbur Project 2017 https:\/\/github.com\/ahmetax\/kalbur (accessed February 2020)."},{"key":"e_1_3_3_39_2","first-page":"9","volume-title":"Proceedings of the 2016 IEEE\/ACM international conference on advances in social networks analysis and mining","author":"Chakraborty A","unstructured":"Chakraborty A, Paranjape B, Kakarla S, et al. Stop clickbait: detecting and preventing clickbaits in online news media. In: Proceedings of the 2016 IEEE\/ACM international conference on advances in social networks analysis and mining, Davis, CA, 18\u201321 August 2016, pp. 9\u201316. New York: IEEE."},{"key":"e_1_3_3_40_2","first-page":"172","volume-title":"Proceedings of the international conference on artificial intelligence: methodology, systems, and applications","author":"Hardalov M","unstructured":"Hardalov M, Koychev I, Nakov P. In search of credible news. In: Proceedings of the international conference on artificial intelligence: methodology, systems, and applications, Varna, Bulgaria, 7\u201310 September 2016, pp. 172\u2013180. New York: Springer."},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5221-8"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_3_3_45_2","first-page":"31","article-title":"Ensemble learning approach for clickbait detection using article headline features","volume":"22","author":"Sisodia DS.","year":"2019","unstructured":"Sisodia DS. Ensemble learning approach for clickbait detection using article headline features. Inform Sci Int J Emerg Transdiscipl 2019; 22: 31\u201344.","journal-title":"Inform Sci Int J Emerg Transdiscipl"},{"key":"e_1_3_3_46_2","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa F","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res 2011; 12: 2825\u20132830.","journal-title":"J Mach Learn Res"},{"issue":"8","key":"e_1_3_3_47_2","first-page":"T1","article-title":"Keras: Deep Learning library for Theano and TensorFlow","volume":"7","author":"Chollet F.","year":"2015","unstructured":"Chollet F. Keras: Deep Learning library for Theano and TensorFlow. Data Sci Cent 2015; 7(8): T1.","journal-title":"Data Sci Cent"}],"container-title":["Journal of Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01655515211007746","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/01655515211007746","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01655515211007746","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:09:18Z","timestamp":1777504158000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/01655515211007746"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,12]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10.1177\/01655515211007746"],"URL":"https:\/\/doi.org\/10.1177\/01655515211007746","relation":{},"ISSN":["0165-5515","1741-6485"],"issn-type":[{"value":"0165-5515","type":"print"},{"value":"1741-6485","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,12]]}}}