"Teens are from mars, adults are from venus": Analyzing and predicting age groups with behavioral characteristics in Instagram

Kyungsik Han, Sanghack Lee, Jin Yea Jang, Yong Jung, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76% - 81% when tested against an independent dataset obtained without using user profiles or tags. Our datasets are available at: https://goo.gl/LqTYNv.

Original languageEnglish (US)
Title of host publicationWebSci 2016 - Proceedings of the 2016 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
Pages35-44
Number of pages10
ISBN (Electronic)9781450342087
DOIs
StatePublished - May 22 2016
Event8th ACM Web Science Conference, WebSci 2016 - Hannover, Germany
Duration: May 22 2016May 25 2016

Publication series

NameWebSci 2016 - Proceedings of the 2016 ACM Web Science Conference

Other

Other8th ACM Web Science Conference, WebSci 2016
CountryGermany
CityHannover
Period5/22/165/25/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Han, K., Lee, S., Jang, J. Y., Jung, Y., & Lee, D. (2016). "Teens are from mars, adults are from venus": Analyzing and predicting age groups with behavioral characteristics in Instagram. In WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference (pp. 35-44). (WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2908131.2908160