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Title: Social curation of content : measurements and models
Author: Zhong, Changtao
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2017
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Social curation is a new trend which has emerged following on the heels of the information glut created by user-generated content revolution. Rather than create new content, social curation allows users to categorise content created by others, and thereby creating and resharing their personal taxonomies of the Web. In this dissertation, we collect a large dataset from Pinterest, arguably the most popular image curation service, and seek to understand the trend on three levels: content, friends and crowds. We first take an empirical look at social curation by mining its content usage. Our data reveals that curation tends to focus on niche items that may not rank highly in popularity and search rankings. Yet, curated items exhibit their own skewed popularity, although most users, or curators, act for personal reasons. At the same time, it also shows that curators with consistent activity and diversity of interests show more social value in attracting followers. This drives us to explore the role of social networks on social curation. We find that social users are more active and are more likely to return soon in Pinterest, indicating a bonding effect enabled by social networks. Then we divide the social network into two subgraphs, according to whether they are created natively or copied from some other established social networks (e.g., Facebook) via a social bootstrapping method. It shows that, when users just join the service, copied network can promote more social interaction, as it initiates a stronger and denser social structure than native network. However, social networks are not critical for information seeking, as a non-trivial number of users’ content are curated from strangers with high interest matching. In fact, this trend also holds for social interaction: Users tend to wean from copied friends to interact more with interest-based native friends over a long-term view. Finally, we understand social curation as a distributed computation process, and examine the relationship between curators and crowds. We show that despite being categorised by individual actions, there is generally a global agreement in implicitly assigning content into a coarse-grained global taxonomy of categories, and furthermore, users tend to specialise in a handful of categories. By exploiting these characteristics, and augmenting with image-related features drawn from a state-of-the-art deep convolutional neural network, we develop a cascade of predictors that together automate a large fraction of curation actions with an end-to-end accuracy of 0.69 (Accuracy@5 of 0.75).
Supervisor: Sastry, Nishanth Ramakrishna Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available