@inproceedings{f0ea37ec55e946f4aba917d6317ca749,
title = "Exploiting category-specific information for image popularity prediction in social media",
abstract = "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.",
keywords = "affective computing, image populality, knowledge extraction, popularity prediction, social network",
author = "Eric Massip and Hidayati, {Shintami Chusnul} and Wen-Huang Cheng and Hua, {Kai Lung}",
year = "2018",
month = nov,
day = "28",
doi = "10.1109/ICMEW.2018.8551545",
language = "English",
series = "2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018",
address = "United States",
note = "null ; Conference date: 23-07-2018 Through 27-07-2018",
}