The Linguistic and Typological Features of Clickbait in Youtube Video Titles-Reference-Cited by-同舟云学术

The Linguistic and Typological Features of Clickbait in Youtube Video Titles

Published:2022-01-01 Issue:1 Volume:8 Page:66-80
ISSN:2450-7563
Container-title:Social Communication
language:en
Short-container-title:

Author:

Kemm Roy¹^ORCID

Affiliation:

1. Nago City Board of Education ; University of Birmingham

Abstract

AbstractThis exploratory study aims to identify which linguistic and typological features commonly associated with clickbait in online news headlines are indicative of clickbait in YouTube video titles. A comparative corpus analysis is conducted to compare YouTube video titles commonly associated with clickbait to titles not associated with clickbait. Results indicate that a majority of the typological and linguistic features associated with clickbait in online news headlines are found to be indicative of clickbait in YouTube video titles. However, the role which each of the features plays seems to differ to that of online news. The findings contribute to the understanding of clickbait in non-news contexts from a linguistics perspective, an area which has been relatively unexplored in the current literature.

Publisher

Walter de Gruyter GmbH

Subject

Education,Cultural Studies

Link

https://www.sciendo.com/pdf/10.2478/sc-2022-0007

Reference33 articles.

1. ALVES, L., ANTUNES, N., AGRICI, O., SOUSA, C., & RAMOS, C. (2016). Click Bait: You won’t believe what happens next!. Fronteiras: Journal of Social, Technological and Environmental Science, 5(2), 196–213. https://doi.org/10.21664/2238-8869.2016V5I2.P196-21310.21664/2238-8869.2016v5i2.p196-213

2. BIYANI, P., TSIOUTSIOULIKLIS, K., & BLACKMER, J. (2016, February 21). “8 Amazing secrets for getting more clicks”: Detecting clickbaits in news streams using article informality. [Conference proceedings]. Proceedings of the 13th AAAI Conference on Artificial Intelligence (pp. 94-100). AAAI-16, Phoenix, Arizona. AAAI Press. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11807/1156910.1609/aaai.v30i1.9966

3. BLOM, J. N. & HANSEN, K. R. (2014). Click bait: Forward-reference as lure in online news headlines. Journal of Pragmatics, 76, 87–100. https://doi.org/10.1016/j.pragma.2014.11.01010.1016/j.pragma.2014.11.010

4. CHAKRABORTY, A., PARANJAPE, A., SOURYA, K., & NILOY, G. (2016). Stop Clickbait: Detecting and preventing clickbaits in online news media”. [Conference proceedings]. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 9–16). 2016 IEEE, San Francisco, California. IEEE. https://doi.org/10.48550/arXiv.1610.0978610.1109/ASONAM.2016.7752207

5. CHEN, Y., CONROY, N. J., & RUBIN, V. L. (2015). Misleading online content: Recognising clickbait as “False News”. [Conference proceedings]. Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection (pp. 15-19). 2015 ACM, Seattle, Washington. ACM. https://doi.org/10.1145/2823465.282346710.1145/2823465.2823467

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