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Detecting agro: Korean trolling and clickbaiting behaviour in online environments
dc.contributor.author | Been Choi, Eun | |
dc.contributor.author | Kim, Jisu | |
dc.contributor.author | Jeong, Dahye | |
dc.contributor.author | Park, Eunil | |
dc.contributor.author | del Pobil, Angel P. | |
dc.date.accessioned | 2022-06-10T07:51:32Z | |
dc.date.available | 2022-06-10T07:51:32Z | |
dc.date.issued | 2022-02-25 | |
dc.identifier.citation | Been Choi, E., Kim, J., Jeong, D., Park, E., & del Pobil, A. P. (2024). Detecting agro: Korean trolling and clickbaiting behaviour in online environments. Journal of Information Science, 50(1), 3-16. | ca_CA |
dc.identifier.issn | 0165-5515 | |
dc.identifier.issn | 1741-6485 | |
dc.identifier.uri | http://hdl.handle.net/10234/197985 | |
dc.description.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. | ca_CA |
dc.format.extent | 14 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | SAGE Publications | ca_CA |
dc.publisher | CILIP | ca_CA |
dc.relation.isPartOf | Journal of Information Science 1–14 | ca_CA |
dc.rights | © The Author(s) 2022 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | agro | ca_CA |
dc.subject | Bi-LSTM | ca_CA |
dc.subject | clickbait | ca_CA |
dc.subject | Korea | ca_CA |
dc.title | Detecting agro: Korean trolling and clickbaiting behaviour in online environments | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1177/01655515221074325 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Ministry of Science, ICT, Korea | ca_CA |
project.funder.name | National Research Foundation (NRF) of Korea | ca_CA |
oaire.awardNumber | IITP-2021-0-02104 | ca_CA |
oaire.awardNumber | IITP-2021-0-02104 | ca_CA |
oaire.awardNumber | 2021R1A4A3022102 | ca_CA |
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