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Detecting YouTube Clickbait with Transformer Models: A Comparative Study

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 4 (2025) · pp. 429–440

DOI
10.22146/ijccs.111977
ISSN
2460-7258, 1978-1520
Published
2025-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Clickbait remains a common strategy on YouTube, where video titles are often crafted to maximize viewer engagement. Although transformer-based machine learning technologies have advanced rapidly, studies that specifically investigate clickbait in YouTube video titles are still rare, even though such titles have unique linguistic characteristics that are shorter, more informal, and more ambiguous than news headlines or other social media texts. This study compares three Transformer models, namely BERT, RoBERTa, and XLNet, for the task of clickbait detection using two benchmark datasets. Each model was fine-tuned and evaluated using standard classification metrics, with additional analyses on training and inference efficiency. The results show that all three models achieved accuracy above 95 percent. RoBERTa achieved the best performance on the Chaudhary dataset (99.84 percent), while BERT cased performed best on the Vierti dataset (96.91 percent). In contrast, XLNet lagged in both accuracy and computational efficiency, with inference times exceeding six seconds per batch. This study demonstrates a 1.31 percent improvement in accuracy compared to previous SVM-based methods and provides a comprehensive evaluation of three Transformer architectures in the YouTube context, offering empirical guidance for more effective clickbait detection.

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links · 2 across different organisations

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  • Universitas Ciputra
  • Universitas Airlangga
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Jason Miracle GunawanUniversitas Ciputra1
Christopher AndreasUniversitas Airlangga1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Detecting YouTube Clickbait with Transformer Models: A Comparative Study</dc:title>
	<dc:creator>Samuel, Bryan</dc:creator>
	<dc:creator>Saputri, Theresia Ratih Dewi</dc:creator>
	<dc:subject xml:lang="en-US">computer science</dc:subject>
	<dc:subject xml:lang="en-US">Clickbait; Youtube; Transformer; RoBERTa; Text Classification</dc:subject>
	<dc:description xml:lang="en-US">Clickbait remains a common strategy on YouTube, where video titles are often crafted to maximize viewer engagement. Although transformer-based machine learning technologies have advanced rapidly, studies that specifically investigate clickbait in YouTube video titles are still rare, even though such titles have unique linguistic characteristics that are shorter, more informal, and more ambiguous than news headlines or other social media texts. This study compares three Transformer models, namely BERT, RoBERTa, and XLNet, for the task of clickbait detection using two benchmark datasets. Each model was fine-tuned and evaluated using standard classification metrics, with additional analyses on training and inference efficiency. The results show that all three models achieved accuracy above 95 percent. RoBERTa achieved the best performance on the Chaudhary dataset (99.84 percent), while BERT cased performed best on the Vierti dataset (96.91 percent). In contrast, XLNet lagged in both accuracy and computational efficiency, with inference times exceeding six seconds per batch. This study demonstrates a 1.31 percent improvement in accuracy compared to previous SVM-based methods and provides a comprehensive evaluation of three Transformer architectures in the YouTube context, offering empirical guidance for more effective clickbait detection.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Ciputra</dc:contributor>
	<dc:date>2025-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/111977</dc:identifier>
	<dc:identifier>10.22146/ijccs.111977</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 4 (2025): October; 429-440</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/111977/42793</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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