Proceedings ArticleDOI
Discovering User-Communities and Associated Topics from YouTube
Sumit Negi,Ramnath Balasubramanyan,Santanu Chaudhury +2 more
- pp 1958-1963
TLDR
An unsupervised method is proposed that jointly models "social" interaction and content metadata in YouTube to discover user-communities and the nature of topics beings discussed in these communities.Abstract:
Most of the popular multimedia sharing web-sites such as YouTube, Flickr etc not only allow users to author and upload content but also facilitate "social" networking amongst users. These social interactions can be in the form of - user-to-user interactions i.e. adding existing users to friend or contact list or user-to-content interactions : commenting on a video or picture, marking a picture/video as "favorite", subscribing to a user created "channel" etc. Analyzing these social interactions jointly with the content metadata (such as the description of the video, keywords associated with the image/video etc) can reveal interesting insights about user activity on these social media platforms. In this paper, we propose an unsupervised method that jointly models "social" interaction and content metadata in YouTube to discover user-communities and the nature of topics beings discussed in these communities. We report the effectiveness of the proposed method on real-world dataset.read more
Citations
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Proceedings ArticleDOI
Interplay between video recommendations, categories, and popularity on YouTube
TL;DR: It is found that about 40% of the video recommendations come from categories other than that of the original video, with Entertainment being the most preferred cross-linked category, and popularity measures including the number of views and comments strongly impact video recommendations.
Journal ArticleDOI
Bibliometric Analysis of Latent Dirichlet Allocation
Mohit Garg,Priya Rangra +1 more
TL;DR: The co-occurrence analysis of keywords indicated that text mining and machine learning were dominant topics in LDA research with significant interest in social media.
References
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Proceedings Article
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Proceedings ArticleDOI
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Posted Content
Mixed membership stochastic blockmodels
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