Title :
New factors for identifying influential bloggers
Author :
Teng-Sheng Moh ; Shola, SivaNaga Prasad
Author_Institution :
Dept. of Comput. Sci., San Jose State Univ. San Jose, San Jose, CA, USA
Abstract :
Blogs have become a major communication media, and have recently been extremely effective in making political and social changes. It is therefore vital to recognize powerful, persuasive bloggers in a web community. In this work we examine existing models for identifying influential bloggers, and construct an improved model based on two new factors: uniqueness and FacebookCount. The former measures the originality of a post, combining with outlink count they represent the novelty of the post. The latter reflects the influence of emerging social network platforms, and can be extended to include twitter share, G +1´s, etc. The proposed model also adopts other effectual factors including the number of inlinks, outlinks and comments, the timing of posts and of comments, and the influence of commenters. In addition, to capture the true influence of a post we mine through each comment on a post to identify the sentiment, or the tone of the comment. The experiments show that, comparing with an existing model [3], the proposed approach is able to capture the true influence, and to give bloggers distinctive rankings. We believe that the two new factors are timely and are significant for identifying influential bloggers in the blogosphere.
Keywords :
data mining; social networking (online); FacebookCount factor; Twitter share; Web community; blogosphere; comment mining; commenter influence; communication media; effectual factors; influential blogger identification; inlink; outlink count; post originality measurement; post timing; sentiment identification; social network platforms; uniqueness factor; Blogs; Communities; Facebook; Indexes; Measurement; Productivity; Blogosphere; Influential Bloggers; Social Networks;
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
DOI :
10.1109/BigData.2013.6691792