Detecting agro: Korean trolling and clickbaiting behaviour in online environments

This article presents one of the first approaches to provide the understanding of agro (one of the unique eye-attracting cues) headlines and thumbnails in online video sharing platform, YouTube. We annotated 1881 headlines and thumbnails, based on agro and the type of agro. Then, we experimented wit...

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Bibliographic Details
Published inJournal of information science Vol. 50; no. 1; pp. 3 - 16
Main Authors Been Choi, Eun, Kim, Jisu, Jeong, Dahye, Park, Eunil, del Pobil, Angel P
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.02.2024
Bowker-Saur Ltd
Subjects
Accuracy
Machine learning
Bi-LSTM
Agro
clickbait
Korea
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Summary:This article presents one of the first approaches to provide the understanding of agro (one of the unique eye-attracting cues) headlines and thumbnails in online video sharing platform, YouTube. We annotated 1881 headlines and thumbnails, based on agro and the type of agro. Then, we experimented with machine learning models to classify agro data from the non-agro data. With a bidirectional long short-term memory (Bi-LSTM) model, we achieved 84.35% of accuracy in detecting agro headlines and 82.80% of accuracy in detecting agro thumbnails. We believe that the automatic detection of agro headlines can allow users to have better experience in browsing through and getting the content that they want online.
ISSN:0165-5515
1741-6485
DOI:10.1177/01655515221074325