TY - GEN
T1 - Popularity meter
T2 - 25th ACM International Conference on Multimedia, MM 2017
AU - Hidayati, Shintami Chusnul
AU - Chen, Yi Ling
AU - Yang, Chao Lung
AU - Hua, Kai Lung
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - Social media websites have become an important channel for content sharing and communication between users on social networks. The shared images on the websites, even the ones from the same user, tend to receive a quite diverse distribution of views. This raises the problem of image popularity prediction on social media. To address this important research topic, we explore three essential components that have considerable impact of the image popularity, which are user profile, post metadata, and photo aesthetics. Moreover, we make use of state-of-the-art predictive modeling approaches to demonstrate the effectiveness of our proposed features in predicting image popularity. We then evaluate the proposed method through a large number of real image posts from Flickr. The experimental results show significant statistical evidence that incorporating the proposed features with ensemble learning method that combines predictions from support vector regression (SVR) and classification and regression tree (CART) models offers a satisfactory popularity prediction. By understanding the social behavior and the underlying structure of content popularity, our research results can also contribute to designing better algorithms for important applications like content recommendation and advertisement placement.
AB - Social media websites have become an important channel for content sharing and communication between users on social networks. The shared images on the websites, even the ones from the same user, tend to receive a quite diverse distribution of views. This raises the problem of image popularity prediction on social media. To address this important research topic, we explore three essential components that have considerable impact of the image popularity, which are user profile, post metadata, and photo aesthetics. Moreover, we make use of state-of-the-art predictive modeling approaches to demonstrate the effectiveness of our proposed features in predicting image popularity. We then evaluate the proposed method through a large number of real image posts from Flickr. The experimental results show significant statistical evidence that incorporating the proposed features with ensemble learning method that combines predictions from support vector regression (SVR) and classification and regression tree (CART) models offers a satisfactory popularity prediction. By understanding the social behavior and the underlying structure of content popularity, our research results can also contribute to designing better algorithms for important applications like content recommendation and advertisement placement.
KW - Affective computing
KW - Image popularity
KW - Knowledge extraction
KW - Popularity prediction
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85035195075&partnerID=8YFLogxK
U2 - 10.1145/3123266.3127903
DO - 10.1145/3123266.3127903
M3 - Conference contribution
AN - SCOPUS:85035195075
T3 - MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
SP - 1918
EP - 1923
BT - MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
PB - Association for Computing Machinery, Inc
Y2 - 23 October 2017 through 27 October 2017
ER -