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The International Research & Innovation Forum

RIIFORUM 2019: Research & Innovation Forum 2019 pp 101–108Cite as

Clickbait in Education—Positive or Negative? Machine Learning Answers

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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.

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References

  1. https://www.britannica.com/topic/yellow-journalism

  2. Thiel, K.: Avoiding Clickbait. Cavendish Square Publishing, LLC (2018)

    Google Scholar 

  3. 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)

  4. Ahmed, S.: BYOD, personal area networks (PANs) and IOT: threats to Patients Privacy. arXiv preprint arXiv:1902.06462 (2019)

  5. 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)

  6. Roelofs, P., Gallien, M.: Clickbait and impact: how academia has been hacked. Impact Soc. Sci. Blog (2017)

    Google Scholar 

  7. Gilley, B.: The case for colonialism. Acad. Quest. 31(2), 167–185 (2018)

    CrossRef  Google Scholar 

  8. https://ocw.mit.edu

  9. 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)

    Google Scholar 

  10. Loewenstein, G.: The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116(1), 75 (1994)

    CrossRef  Google Scholar 

  11. Vijgen, B.: The listicle: an exploring research on an interesting shareable new media phenomenon. Stud. Univ. Babes-Bolyai, Ephemer. 59(1) (2014)

    Google Scholar 

  12. Blom, J.N., Hansen, K.R.: Click bait: forward-reference as lure in online news headlines. J. Pragmat. 76, 87–100 (2015)

    CrossRef  Google Scholar 

  13. Potthast, M., Köpsel, S., Stein, B., Hagen, M.: Clickbait detection. In: European Conference on Information Retrieval, pp. 810–817. Springer, Cham (2016)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. https://clickbait-challenge.org

  18. 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)

    Google Scholar 

  19. Rajput, A., Ahmed, S.: Making a case for social media corpus for detecting depression. arXiv preprint arXiv:1902.00702 (2019)

  20. 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)

    Google Scholar 

  21. Zhou, Y.: Clickbait detection in tweets using self-attentive network. arXiv preprint arXiv:1710.05364 (2017)

  22. 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)

  23. Lockwood, G.: Academic clickbait: articles with positively-framed titles, interesting phrasing, and no wordplay get more attention online. Winnower 3 (2016)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. Lytras, M., Visvizi, A., Daniela, L., Sarirete, A., Ordonez De Pablos, P.: Social networks research for sustainable smart education. Sustainability 10(9), 2974 (2018)

    CrossRef  Google Scholar 

  26. 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)

    CrossRef  Google Scholar 

  27. Lytras, M., Visvizi, A., Damiani, E., Mthkour, H.: The cognitive computing turn in education: prospects and application (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Engineering, Effat University, Jeddah, Saudi Arabia

    Adil E. Rajput

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  1. Adil E. Rajput
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Correspondence to Adil E. Rajput .

Editor information

Editors and Affiliations

  1. Research & Innovation Institute (Rii), Warsaw, Poland

    Anna Visvizi

  2. Deree College, The American College of Greece, Athens, Greece

    Miltiadis D. Lytras

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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

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  • DOI: https://doi.org/10.1007/978-3-030-30809-4_10

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