Comparison: A Latent Variable Model for Geographic Lexical Variation and A probabilistic approach to spatiotemporal theme pattern mining on weblogs

Papers

1. A Latent Variable Model for Geographic Lexical Variation
2. A probabilistic approach to spatiotemporal theme pattern mining on weblogs

Problem

Hassan et al. were trying to target the problem of ranking documents in a set based on their similarity to identify the representative blogs in a given set usually based on different topics, similar to blog summarization.

Arguello et al. were trying to target the problem of blog retrieval - retrieving ranked list of blogs relevant to the given user query.

Basically these two papers are trying to achieve different goals. Hassan et al. proposed methods of ranking blogs within a given topic collection. This ranking of blogs based on their importance in a topic collection can be useful for blog search tasks. Arguello et al. experimented with various models to try and improve the blog search results.

Big Idea

The two papers differ in their respective central ideas as they both try to solve different problems as mentioned in Problem section above. They do use a common data set to evaluate their experiments, but their results can't be compared due to the difference in the problem they are addressing.

Method

Hassan et al. have used BlogRank algorithm to rank blogs according to their popularity which considers lexical similarity between two blogs to identify graphical links between nodes representing the two blogs. And then based on the iterative algorithm like a random walk of this graph, it determines the rank for each blog. It also enhances diversity by penalizing blogs which are similar to a higher ranked blog.

Arguello et al. have used different blog representation models and query expansion techniques to enhance the blog retrieval results. They have tried representation models considering entire blog as one large document or treating each blog post as a small document within a collection. For query expansion they experimented with the traditional pseudo-relevance feedback model and another method where they extended the query using ranked anchor text from Wikipedia corpus related to the base query.

Dataset Used

Hassan et al. used the TREC BLOG06 and UCLA Blogocenter datasets for experiments, whereas Arguello et al. used only the TREC BLOG06 dataset for its experiments.

Other Discussions

Both the papers show significant improvement in results from baseline with their proposed methods. Both these papers deal with problems which together are essential in better understanding of the blogosphere and will be helpful in blog retrieval and summarization.

Other Questions

1. How much time did you spend reading the (new, non-wikified) paper you summarized? 3 hours
2. How much time did you spend reading the old wikified paper? 1 hour 30 min
3. How much time did you spend reading the summary of the old paper? 20 min
4. How much time did you spend reading background material? 1 hour
5. Was there a study plan for the old paper? No
   1. if so, did you read any of the items suggested by the study plan? and how much time did you spend with reading them? Study Plan Not Available
6. Give us any additional feedback you might have about this assignment.
   • I think, it might be useful to compare papers which address same problems. In this case, the two papers were trying to deal with different problems and hence there wasn't much to compare between the two. The time spent on this task can also be reduced if both the papers have been summarized by the same person comparing it, but that would depend on whether or not we want a different opinion for an existing summary (provided the comparison is between papers addressing same problems).