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Been Choi E, Kim J, Jeong D, Park E, del Pobil AP. Detecting agro: Korean trolling and clickbaiting behaviour in online environments. J Inf Sci 2022. [DOI: 10.1177/01655515221074325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
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.
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Affiliation(s)
- Eun Been Choi
- Department of Interaction Science, Sungkyunkwan University, Korea; Department of Computer Science and Engineering, Jaume I University, Spain
| | - Jisu Kim
- AI Lab, Fassto, Korea; AI Team, Raon Data, Korea
| | - Dahye Jeong
- Department of Computer Science and Engineering, Jaume I University, Spain; Department of Applied Artificial Intelligence, Sungkyunkwan University, Korea
| | - Eunil Park
- Department of Interaction Science, Sungkyunkwan University, Korea; Department of Applied Artificial Intelligence, Sungkyunkwan University, Korea
| | - Angel P del Pobil
- Department of Computer Science and Engineering, Jaume I University, Spain
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