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 language | English (US) |
|---|---|
| Title of host publication | WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 35-44 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781450342087 |
| DOIs | |
| State | Published - May 22 2016 |
| Event | 8th ACM Web Science Conference, WebSci 2016 - Hannover, Germany Duration: May 22 2016 → May 25 2016 |
Publication series
| Name | WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference |
|---|
Other
| Other | 8th ACM Web Science Conference, WebSci 2016 |
|---|---|
| Country | Germany |
| City | Hannover |
| Period | 5/22/16 → 5/25/16 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
Cite this
}
"Teens are from mars, adults are from venus" : Analyzing and predicting age groups with behavioral characteristics in Instagram. / Han, Kyungsik; Lee, Sanghack; Jang, Jin Yea; Jung, Yong; Lee, Dongwon.
WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference. Association for Computing Machinery, Inc, 2016. p. 35-44 (WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - "Teens are from mars, adults are from venus"
T2 - Analyzing and predicting age groups with behavioral characteristics in Instagram
AU - Han, Kyungsik
AU - Lee, Sanghack
AU - Jang, Jin Yea
AU - Jung, Yong
AU - Lee, Dongwon
PY - 2016/5/22
Y1 - 2016/5/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84976393640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84976393640&partnerID=8YFLogxK
U2 - 10.1145/2908131.2908160
DO - 10.1145/2908131.2908160
M3 - Conference contribution
AN - SCOPUS:84976393640
T3 - WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference
SP - 35
EP - 44
BT - WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference
PB - Association for Computing Machinery, Inc
ER -