TY - GEN
T1 - Exploiting category-specific information for image popularity prediction in social media
AU - Massip, Eric
AU - Hidayati, Shintami Chusnul
AU - Cheng, Wen Huang
AU - Hua, Kai Lung
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/28
Y1 - 2018/11/28
N2 - Social media has become an important part of each individual's life and an invaluable tool for companies to know their customers better in the market. The problem of popularity prediction in social media has been studied extensively over the past few years. Yet, it is still a challenging task due to various factors, including the difficulty to measure the preference of viewers towards specific post contents, the influence of user popularity, and the properties of social media itself. Accordingly, this paper focuses on image popularity prediction by analyzing early popularity patterns of posts in the same content category, fused with user information data. We conduct extensive experiments on a dataset crawled from Instagram. The experimental results show that our proposed model achieves considerable performance in predicting the number of likes of an Instagram input photo. Moreover, we also provide in-depth analysis of the importance of category-specific information in popularity prediction.
AB - Social media has become an important part of each individual's life and an invaluable tool for companies to know their customers better in the market. The problem of popularity prediction in social media has been studied extensively over the past few years. Yet, it is still a challenging task due to various factors, including the difficulty to measure the preference of viewers towards specific post contents, the influence of user popularity, and the properties of social media itself. Accordingly, this paper focuses on image popularity prediction by analyzing early popularity patterns of posts in the same content category, fused with user information data. We conduct extensive experiments on a dataset crawled from Instagram. The experimental results show that our proposed model achieves considerable performance in predicting the number of likes of an Instagram input photo. Moreover, we also provide in-depth analysis of the importance of category-specific information in popularity prediction.
KW - affective computing
KW - image populality
KW - knowledge extraction
KW - popularity prediction
KW - social network
UR - http://www.scopus.com/inward/record.url?scp=85059972033&partnerID=8YFLogxK
U2 - 10.1109/ICMEW.2018.8551545
DO - 10.1109/ICMEW.2018.8551545
M3 - Conference contribution
AN - SCOPUS:85059972033
T3 - 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
BT - 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
Y2 - 23 July 2018 through 27 July 2018
ER -