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Sensors 2017, 17(3), 631; doi:10.3390/s17030631

On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data

Multimedia Computing Research Laboratory (MCRLab); School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
College of Computer Science and Engineering (CCSE), Taibah University, Medina 42353, Saudi Arabia
Author to whom correspondence should be addressed.
Academic Editors: M. Shamim Hossain and Athanasios V. Vasilakos
Received: 20 November 2016 / Revised: 15 March 2017 / Accepted: 16 March 2017 / Published: 19 March 2017
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
View Full-Text   |   Download PDF [1867 KB, uploaded 19 March 2017]   |  


The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the prediction of image popularity on social photo-sharing site. View Full-Text
Keywords: social sensors; social sensory data; social image; popularity prediction; enhanced living environment; social media; social networks social sensors; social sensory data; social image; popularity prediction; enhanced living environment; social media; social networks

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Aloufi, S.; Zhu, S.; El Saddik, A. On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data. Sensors 2017, 17, 631.

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