Stay alert: Forecasting the risks of sexting in Korea using social big data

Juyoung Song, Tae Min Song, Jin Ree Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Youth sexting, which is commonly defined as the intimate image sharing of persons under 18, is an emerging phenomenon that has garnered significant attention in South Korea and in particular, the South Korean government. Widely recognized for its potential to generate undue harm, the South Korean government has initiated a movement determined to block the participation of obscene content sharing between youths under the age of 18. While there may be different avenues to examine this phenomenon from, an approach notably absent from this list is the use of big data and data mining information produced via the dispersion of the Internet and social media. Using social big data, the study found that teenagers sexting in hopes of obtaining a higher volume of attention among friends; file sharing is more frequented than image distribution through sexting; and transactions without “adult pornography” and with “smishing” were the most influential in addressing the risks of sexting in South Korea. While big data and data mining do not make any inferences themselves, the benefits of analyzing social big data lies in its ability to incorporate a much larger volume of data and confirm the thoughts of a diverse range of participants.

Original languageEnglish (US)
Pages (from-to)294-302
Number of pages9
JournalComputers in Human Behavior
Volume81
DOIs
StatePublished - Apr 1 2018

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Korea
Republic of Korea
Data Mining
Erotica
Social Media
Data mining
Internet
South Korea
Government
Big data
Person
Participation
Inference
Harm
World Wide Web
Teenagers
File Sharing
Pornography
Obscene

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

Cite this

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Stay alert : Forecasting the risks of sexting in Korea using social big data. / Song, Juyoung; Song, Tae Min; Lee, Jin Ree.

In: Computers in Human Behavior, Vol. 81, 01.04.2018, p. 294-302.

Research output: Contribution to journalArticle

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