Eigen-Trend: Trend Analysis in the Blogosphere based on Singular Value Decompositions

Here is the paper.

Situation: The blogosphere provides free large-scale information sources from which businesses can quickly learn trends (e.g., opinions and complaints from their customers). How? For example, by keeping track of how often relevant keywords are mentioned across blogs (summing up the occurrence of keywords).

Complication: This simple way of extracting trends has tree problems:
1) Different blogs contribute to the trend differently.
2) For the same keyword, different groups of blogs may have different interests.
3) Can we directly study and extract meaningful trends from such a dynamically changing blog graph structure? The blogosphere can be considered as a blog graph where the nodes are blogs and the links reflect endorsements and interactions among blogs. In addition, such a blog graph is changing with time as a result of the development of internal relationships (e.g., interactions among blogs) and external events (e.g., breaking news).

Solution: The authors propose eigen-trends, temporal indicators derived through singular value decomposition, that take differences among individual blogs in consideration. The key idea is to represent the observed data as a combination of information that captures temporal changes of the underlying data (i.e., eigen-trends) and information that captures the characteristics of individual bloggers (e.g., authority).