SCIENCE@home

Detecting agro: Korean trolling and clickbaiting behaviour in online environments

Eun Been Choi,Jisu Kim,Dahye Jeong,Eunil Park,A. del Pobil

Published 2022 in Journal of information science

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.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-52 of 52 references · Page 1 of 1

CITED BY

Showing 1-5 of 5 citing papers · Page 1 of 1