Future Link Prediction in the Blogosphere for Recommendation
Author(s) -
Shanchan Wu,
Louiqa Raschid,
William Rand
Publication year - 2011
Publication title -
ssrn electronic journal
Language(s) - English
Resource type - Journals
ISSN - 1556-5068
DOI - 10.2139/ssrn.1906467
Subject(s) - blogosphere , hyperlink , computer science , social media , microblogging , link analysis , world wide web , event (particle physics) , proxy (statistics) , information retrieval , channel (broadcasting) , graph , the internet , data science , web page , computer network , physics , theoretical computer science , quantum mechanics , machine learning
The phenomenal growth in both scale and importance of social media such as blogs, micro-blogs and user-generated content, has created a need for tools that monitor information diffusion and make recommendations within these platforms. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. There are two types of links within the blogosphere; one from blog post to blog post, and another from blog post to blog channel (an event stream of blog posts). These links can be viewed as a proxy for the flow of information between blog channels and to reflect influence. Given this assumption about links, the ability to predict future links can facilitate the monitoring of information diffusion, making recommendations, and word-of-mouth (WOM) marketing. We propose different methods for link predictions and we evaluate these methods on an extensive blog dataset.
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