The language means of comicality in clickbait headings

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The analysis of material presented in the media discourse demonstrates significant changes in the intentionality of the journalistic text, which are reflected in establishing contacts so as to grab and retain the reader’s attention. This feature of modern media text is represented in changing genre preferences, speech tactics and strategies, and, consequently, selecting and combining linguistic means. One of the manifestations of this trend is the phenomenon of clickbait, which is a communicative act of promising to continue communication. This article is dedicated to the clickbait with the semantics of comicality. The collected from the Russian-language Internet research material includes clickbait headings that promise a certain funny content. The study revealed that a clickbait model includes the following semantic components: a stimulating utterance of the subject of speech seeking to involve the reader in the humorous nature of hypertext; the verbal and non-verbal markers of the object of laughter; markers, which reflect Internet user’s involvement in the communicative act. The analysis of relationship between the components of a clickbait model resulted in specifying four types of clickbait headlines: 1) narrative headlines, which invite the reader to laugh what some other readers have already laughed at; 2) offering headlines suggesting some comic entertainment; 3) allusive clickbaits that hint on the possibility to continue amusing reading; 4) nominative clickbaits, which name the expected laughing reaction to the presentation of some objects.

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Clickbait, comicality, media text, semantics, speech act

Короткий адрес: https://readera.org/149137952

IDR: 149137952   |   DOI: 10.15688/jvolsu2.2021.3.13

Список литературы The language means of comicality in clickbait headings

  • Ageeva G.A., Smyshliaeva V.A., 2019. Clickbait: etimologiia, semantika, sredstva vyrazheniia [Clickbait: Etymology, Semantics, Expression Means]. Molodezhnyi vestnik Irkutskogo natsional 'nogo issledovatel 'skogo tekhniches-kogo universiteta [Young Researchers' Journal of ISTU], vol. 9, iss. 1, pp. 153-158.
  • Agrawal A., 2016. Clickbait Detection Using Deep Learning. 2nd IEEE International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India. S. l., IEEE Publ., pp. 268-272.
  • Al Asaad B., Erascu M., 2018. A Tool for Fake News Detection. 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), September 20-23, Timisoara, Romania. S. l., IEEE Publ., pp. 379-386. DOI: 10.1109/SYNASC.2018. 00064.
  • Anand A., Chakraborty T., Park N., 2017. We Used Neural Networks to Detect Clickbaits: You Won't Believe What Happened Next! 39thEuropean Conference on Information Retrieval (ECIR), April 8-13, Aberdeen, United Kingdom. S. l., Springer, pp. 541547. DOI: 10.1007/978-3-319-56608-5_46. (Lecture Notes in Computer Science (LNCS), no. 10193).
  • Bakhtin M.M., 1996. Problema rechevykh zhanrov [The Problem of Speech Genres]. Sobranie sochinenii. V 7 t. [Complete Works. In 7 Vols.]. Moscow, Russkie slovari, vol. 5, pp. 159-206.
  • Brzozowska D., 2013. Demotivators as a New Type of Media Texts. Medialingvistika [Media Linguistics]. Saint Petersburg, Izd-vo Sankt-Peterburgskogo gosudarstvennogo universiteta, iss. 2, pp. 124-130.
  • Chanysheva Z.Z., 2016. Informatsionnye tekhnologii smyslovykh iskazhenii v klikbeit-zagolovkakh [Information Technologies of Semantic Distortions in Clickbait Headlines]. Vestnik Permskogo natsional 'nogo issledovatel 'skogo politekhnicheskogo universiteta. Problemy iazykoznaniia i pedagogiki [PNRPU Linguistics and Pedagogy Bulletin], no. 4, pp. 54-62. DOI: 0.15593/2224-9389/2016.4.5.
  • Daoud D.M., El-Seoud M.S.A., 2019. An Effective Approach for Clickbait Detection Based on Supervised Machine Learning Technique. International Journal of Online and Biomedical Engineering (iJOE), vol. 15, iss. 3, pp. 21-32. DOI: 10.3991/ijoe.v15i03.9843.
  • Duskaeva L.R., ed., 2018. Medialingvistika v terminakh i poniatiiakh : slov. -sprav. [Media Linguistics in Terms and Concepts. Reference Dictionary]. Moscow, Flinta Publ. 442 p.
  • Duskaeva L.R., 2004. Dialogicheskaia priroda gazetnykh rechevykh zhanrov [The Dialogical Nature of Newspaper Speech Genres]. Perm, Izd-vo Permskogo universiteta. 276 p.
  • Fu J., Liang L., Zhou X., Zheng J., 2017. A Convolutional Neural Network for Clickbait Detection. 4th International Conference on Information Science and Control Engineering (ICISCE), 14November, Changsha, China. S. l., IEEE Publ., pp. 6-10. DOI: 10.1109/ ICISCE.2017.11.
  • Gajda S., Brzozowska D., ed., 2000. Swiathumoru. Opole, Wydawnictwo Uniwersytetu Opolskiego. 628 p.
  • Gavrikova O.A., 2018a. Kriterii vyiavleniia klikbeit-zagolovkov [Identification Criteria of Clickbait Peadlines]. Iazykovye edinitsy v svete sovremennykh nauchnykh paradigm: materialy IV Vseros. nauch.-prakt. konf. s mezhdunar. uchastiem (20 dek. 2018 g.) [Linguistic Units in the Light of Modern Scientific Paradigms. Proceedings of the 4th Scientific and Practical
  • Conference With International Participation, 20 December, 2018]. Ufa, Izd-vo Bashkirskogo gosudarstvennogo universiteta, pp. 23-28.
  • Gavrikova O.A., 2018b. O fastsinativnom vospriiatii klikbeit-form v interdiskursivnom prostranstve [Fascinative Perception of Clickbait Forms in an Interdiscursive Space]. Doklady Bashkirskogo universiteta, vol. 3, iss. 1, pp. 94-99.
  • Gavrikova O.A., 2018c. Smyslovoe iskazhenie informatsii v klikbeit-zagolovkakh v tekstakh politicheskogo mediadiskursa [Semantic Distortion of Information in Clickbait Headers in the Texts of a Political Media Discourse]. Vestnik Bashkirskogo universiteta [Bulletin of Bashkir University], vol. 23, iss. 1, pp. 173-178. DOI: 10.33184/bulletin-bsu-2018.1.28.
  • Geckil A., Mungen A.A., Gundogan E., Kaya M., 2018. A Clickbait Detection Method on News Sites. 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2831 August. S. l., IEEE Publ., pp. 932-937.
  • Khater S.R., Al-Sahlee O.H., Daoud D.M., El-Seoud M.S.A., 2018. Clickbait Detection. 7th International Conference on Software and Information Engineering (ICSIE), British University in Cairo, Egypt, 2-4 May. Cairo, IEEE Publ., pp. 111-115. DOI: 10.1145/3220267.3220287.
  • Molyneux L., Coddington M., 2019. Aggregation, Clickbait and Their Effect on Perceptions of Journalistic Credibility and Quality. Journalism Practice. Taylor & Francis, vol. 14, iss. 4, pp. 429446. DOI: 10.1080/17512786.2019.1628658.
  • Nikolaeva A.V, 2017. Klikbeit: k opredeleniiu poniatiia [Clickbait: Defining the Concept]. Aktual 'nye problemy stilistiki [Actual Problems of Stylistics], no. 3, pp. 146-151.
  • Potthast M., Köpsel S., Stein B., Hagen M., 2016. Clickbait Detection. 38th European Conference on Information Retrieval Research (ECIR), Padua, Italy, 20-23 March. S. l., Springer, pp. 810-817. DOI: 10.1007/978-3-319-30671-1_72. (Lecture Notes in Computer Science (LNCS), no. 9626).
  • Serrano J.G., Romero-Rodríguez L.M., Gómez Á.H., 2018. Análisis del Clickbaiting en Los Titulares de la Prensa Española Contemporánea. Estudio de Caso: Diario El País en Facebook. Estudios Sobre el Mensaje Periodistico, vol. 25, iss. 1, pp. 197-212. DOI: 10.5209/ESMP.63724.
  • Shu K., Wang S., Le T., Lee D., Liu H., 2018. Deep Headline Generation for Clickbait Detection. 18th IEEE International Conference on Data Mining (ICDM), Singapore, 17-20 November. Singapore, IEEE Publ., pp. 467-476. DOI: 10.1109/ ICDM.2018.00062.
  • Sladkevich Zh.R., 2019. Zagolovki-anonsy v setevykh mediaservisakh: mezhdu informirovaniem i klikbeitingom [Announcement Headlines in Network Media Services: Awareness and Clickbaiting]. Medialingvistika [Media Linguistics], vol. 6, iss. 3, pp. 354-369.
  • Solov'ev A., 2018. Klikbeit-zagolovki v reklame: ispol'zovat' nel'zia ignorirovat' [Clickbait Headlines in Ads: Should Be Used and Cannot Be Ignored]. Zhurnalistyka-2018: stan, prablemy i perspektyvy: materyialy 20-i mizhnarodnai navukova-praktychnai kanferentsyi (Minsk, 1516 list. 2018 g.) [Journalism-2018: Current Situation, Issues and Prospective Directions: Proceedings of the 20th International Scientific and Practical Conference (Minsk, 15-16 November, 2018)], Minsk, BDU, pp. 253-255.
  • Vasileva V.V., 2017. "Obeshchanie smeshnogo" kak strategiia privlecheniia massovoi auditoria ["Promising Some Comic Entertainment" as a Strategy to Attract Mass Audience]. Strategicheskie kommunikatsii v biznese i politike: tez. Mezhdunar. nauch. konf. (Sankt-Peterburg, 22-23 noyab., 2017 g.) [Strategic Communication in Business and Politics. Thesis Statements of International Scientific Conference (Saint Petersburg, November 22-23, 2017)]. URL: http://jf. spbu.ru/conf-pr/755 8/7562-gr2. html (accessed 20 December 2019).
  • Vasileva V., Prokofeva N., 2015. Slang Toponyms and Newsmakers' Nicknames as a Communicative Contact and Indicator of Comic Culture in the Modern Russian Journalism. International Review of Management and Marketing, no. 5, pp. 1-10.
  • Vinogradov V.V., 1959. Oyazyke khudozhestvennoy literatury [About the Language of Fiction]. Moscow, Goslitizdat Publ. 656 p.
  • Vol'skaia N.N., 2018. Klikbeit kak sredstvo sozdaniia lozhnoi informatsii v internet-kommunikatsii [Clickbait as a Means of Producing False Information in Internet Communication].
  • Mediaskop [Mediascope], no. 2. URL: http:// www.mediascope.ru/2450 (accessed 19 December 2019). DOI: 10.30547/mediascope.2.2018.12.
  • Wang S., Wu Q., 2017. An Empirical Study on the Clickbait of Data Science Articles in the WeChat Official Accounts. International Conference on Frontier Computing (FC), Osaka, Japan, 1214 July. S. l., Springer, pp. 131-140. DOI: 10.1007/ 978-981-10-7398-4_14. (Lecture Notes in Electrical Engineering (LNEE), no. 464).
  • Wongsap N., Lou L., Jumun S., Prapphan T., Kongyoung S., Kaothanthong N., 2018. Thai Clickbait Headline News Classification and its Characteristic. 2018 International Conference on Embedded Systems and Intelligent Technology and International Conference on Information and Communication Technology for Embedded Systems, (ICESIT-ICICTES), Khon Kaen, Thailand, 7-9May. S. l., IEEE Publ., pp. 1-6. DOI: 10.1109/ICESIT-ICICTES.2018. 8442064.
  • Zannettou S., Chatzis S.P., Papadamou K., Sirivianos M., 2018. The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube. 1st Deep Learning and Security Workshop, co-located with the 39th IEEE Symposium on Security and Privacy, San Francisco, 21-23May. S. l., IEEE Publ., pp. 6369. DOI: 10.1109/SPW.2018.00018.
  • Zheng H.-T., Yao X., Jiang Y., Xia S.-T., Xiao X., 2017. Boost Clickbait Detection Based on User Behavior Analysis. 1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM, Beijing, China, 7-9 July. S. l., Springer, pp. 73-80. DOI: 10.1007/978-3-319-63564-4_6. (Lecture Notes in Computer Science (LNCS), no. 10367).
  • Zheng H.-T., Chen J.-Y., Yao X., Sangaiah A.K., Jiang Y., Zhao C.-Z., 2018. Clickbait Convolutional Neural Network. Symmetry. Multidisciplinary Digital Publishing Institute (MDPI), vol. 10, iss. 5. DOI: 10.3390/sym10050138.
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