Abstract
The topic of clickbait has garnered lot of attention since the advent of social media. Meriam-Webster defines Clickbait as something designed to make readers want to click on a hyperlink especially when the link leads to content of dubious value or interest. Clickbait is used synonymously with terms with negative connotations such as yellow journalism [1], tabloid news etc. Majority of the work in this area has focused on detecting clickbait to stop being presented to the reader. In this work, we look at clickbait in the field of education with emphasis on educational videos that are authored by individual authors without any institutional backing. Such videos can become quite popular with different audiences and are not verified by any expert. We present findings that despite the negative connotation associated with clickbait, the audience value content regardless of the clickbait techniques and have an overall favorable impression. We also establish initial metrics that can be used to gauge the likeness factor for such educational videos/MOOCs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Thiel, K.: Avoiding Clickbait. Cavendish Square Publishing, LLC (2018)
Rajput, A., Brahimi, T.: Characterizing IOMT/personal area networks landscape. In: Lytras, M., et al. (eds.) Innovation in Health Informatics: A Smart Healthcare Primer. Amsterdam, Elsevier, Netherlands (2019). (earlier version available as arXiv preprint arXiv:1902.00675)
Ahmed, S.: BYOD, personal area networks (PANs) and IOT: threats to Patients Privacy. arXiv preprint arXiv:1902.06462 (2019)
Rajput, A.E., Ahmed, S.M.: Big data and social/medical sciences: state of the art and future trends. arXiv preprint arXiv:1902.00705 (2019)
Roelofs, P., Gallien, M.: Clickbait and impact: how academia has been hacked. Impact Soc. Sci. Blog (2017)
Gilley, B.: The case for colonialism. Acad. Quest. 31(2), 167–185 (2018)
Harrison, D.J.: Assessing experiences with online educational videos: converting multiple constructed responses to quantifiable data. Int. Rev. Res. Open Distrib. Learn. 16(1) (2015)
Loewenstein, G.: The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116(1), 75 (1994)
Vijgen, B.: The listicle: an exploring research on an interesting shareable new media phenomenon. Stud. Univ. Babes-Bolyai, Ephemer. 59(1) (2014)
Blom, J.N., Hansen, K.R.: Click bait: forward-reference as lure in online news headlines. J. Pragmat. 76, 87–100 (2015)
Potthast, M., Köpsel, S., Stein, B., Hagen, M.: Clickbait detection. In: European Conference on Information Retrieval, pp. 810–817. Springer, Cham (2016)
Chen, Y., Conroy, N.J., Rubin, V.L.: Misleading online content: recognizing clickbait as false news. In: Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection, pp. 15–19. ACM (2015)
Silverman, C.: Lies, damn lies, and viral content. how news websites spread (and debunk) online rumors, unverified claims, and misinformation. Tow Cent. Digit. J. 168(4), 134–140 (2015)
Rubin, V.L., Conroy, N.J., Chen, Y.: Towards news verification: deception detection methods for news discourse. In: Hawaii International Conference on System Sciences (2015)
Potthast, M., Gollub, T., Komlossy, K., Schuster, S., Wiegmann, M., Fernandez, E.P.G., Hagen, M., Stein, B.: Crowdsourcing a large corpus of clickbait on twitter. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1498–1507 (2018)
Rajput, A., Ahmed, S.: Making a case for social media corpus for detecting depression. arXiv preprint arXiv:1902.00702 (2019)
Chakraborty, A., Paranjape, B., Kakarla, S., Ganguly, N.: Stop clickbait: detecting and preventing clickbaits in online news media. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 9–16. IEEE (2016)
Zhou, Y.: Clickbait detection in tweets using self-attentive network. arXiv preprint arXiv:1710.05364 (2017)
Rajput, A.: Natural language processing, sentiment analysis and clinical analytics. In: Lytras, M., et al. (eds.) Innovation in Health Informatics: A Smart Healthcare Primer. Elsevier, Amsterdam, Netherlands (2019). (available as arXiv preprint arXiv:1902.00679)
Lockwood, G.: Academic clickbait: articles with positively-framed titles, interesting phrasing, and no wordplay get more attention online. Winnower 3 (2016)
Visvizi, A., Lytras, M.D., Daniela, L.: The future of innovation and technology in education: a case for restoring the role of the teacher as a mentor. In: The Future of Innovation and Technology in Education: Policies and Practices for Teaching and Learning Excellence, pp. 1–8. Emerald Publishing Limited (2018)
Lytras, M., Visvizi, A., Daniela, L., Sarirete, A., Ordonez De Pablos, P.: Social networks research for sustainable smart education. Sustainability 10(9), 2974 (2018)
Lytras, M.D., Aljohani, N.R., Visvizi, A., Ordonez De Pablos, P., Gasevic, D.: Advanced decision-making in higher education: learning analytics research and key performance indicators (2018)
Lytras, M., Visvizi, A., Damiani, E., Mthkour, H.: The cognitive computing turn in education: prospects and application (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rajput, A.E. (2019). Clickbait in Education—Positive or Negative? Machine Learning Answers. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-30809-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30808-7
Online ISBN: 978-3-030-30809-4
eBook Packages: EducationEducation (R0)