Name
Abstract | ||
|---|---|---|
The weblog, or blog, has become a popular form of social media, through which authors can write posts, which can in turn generate feedback in the form of user comments. When considered in totality, a collection of blogs can thus be viewed as a sort of informal collection of mass sentiment and opinion. An obvious topic of interest might be to mine this collection to obtain some gauge of public sentiment over the wide variety of topics contained therein. However, the sheer size of the so-called blogosphere, combined with the fact that the subjects of posts can vary over a practically limitless number of topics poses some serious challenges when any meaningful analysis is attempted. Namely, the fact that largely anyone with access to the Internet can author their own blog, raises the serious issue of credibility---should some blogs be considered to be more influential than others, and consequently, when gauging sentiment with respect to a topic, should some blogs be weighted more heavily than others? In addition, as new posts and comments can be made on almost a constant basis, any blog analysis algorithm must be able to handle such updates efficiently. In this paper, we give a formalization of the blog model. We give formal methods of quantifying sentiment and influence with respect to a hierarchy of topics, with the specific aim of facilitating the computation of a per-topic, influence-weighted sentiment measure. Finally, as efficiency is a specific endgoal, we give upper bounds on the time required to update these values with new posts, showing that our analysis and algorithms are scalable. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1964858.1964866 | SOMA@KDD |
Keywords | Field | DocType |
own blog,social media analytics,influence,quantifying sentiment,public sentiment,meaningful analysis,informal collection,sentiment,influence-weighted sentiment measure,formal methods,mass sentiment,new post,blog analysis algorithm,blog model,formal method,feedback,upper bound,efficiency,algorithms,social media | Data mining,Social media analytics,Social media,Sentiment analysis,Computer science,sort,Artificial intelligence,Formal methods,Blogosphere,Hierarchy,Machine learning,The Internet | Conference |
Citations | PageRank | References |
3 | 0.38 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Hui | 1 | 18 | 2.73 |
| Michelle Gregory | 2 | 129 | 11.35 |