With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. 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. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. ![]() Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Social data are different from the data collected from physical sensors in the fact that they exhibit special characteristics that pose new challenges. 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. ![]() This provides an opportunity for us to understand human behavior and enhance the services provided for both the real 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. The increase in the popularity of social media has shattered the gap between the physical and virtual worlds.
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