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

==Papers==
#[http://malt.ml.cmu.edu/mw/index.php/A_Latent_Variable_Model_for_Geographic_Lexical_Variation A Latent Variable Model for Geographic Lexical Variation]
#[http://malt.ml.cmu.edu/mw/index.php/Q._Mei,_C._Liu,_H._Su,_and_C._X_Zhai._2006._A_probabilistic_approach_to_spatiotemporal_theme_pattern_mining_on_weblogs._In_Proceedings_of_WWW. A probabilistic approach to spatiotemporal theme pattern mining on weblogs ]

== Problem ==

Jacob et al. aims to analyze the variation in the usage of words in vernacular wrt geography. In particular, it analyzes lexical variation by both topic and geography. It also separates regions into coherent linguistic communities. Also it can predict with some accuracy the location of the author from raw text.


Q. Mei et al. aims to analyze webblogs by analyzing their spatiotemporal petterns. In particular, it addresses the problem that former approaches in finding subtopics for weblogs only considering either spatial information or temporal information. 

== Method ==

Jacob et al. use an enhanced edition of LDA by considering location information in modeling word distribution and assigning a probabilistic model for location and document. 

Q. Mei et al. designed their model based on pLSI and give no probabilistic model for document. 

== Dataset Used ==
Jacob et al. use [[UsesDataset::GeoTagged Twitter Dataset]] and Q. Mei et al. use [[UsesDataset:: Hurricane Katrina]],[[UsesDataset:: Hurricane Rita]],[[UsesDataset:: IPod Nano]]


== Big Idea ==

These two papers are different in all three above aspects, i.e problem addressed, methods, dataset used. 

Problem: Jacob et al. try to find topics that related to a specific user by incorporating its location information while Q. Mei et al. aim at finding subtopics in different time and locations from documents that have the same topics. 

Method: Jacob et al. use a LDA type model while Q. Mei et al. adapt a pLSI type mehtod. 

Dataset: Because of different problem they address, the data set they used are very different. Jacob et al. use twitter type documents, which are very short. Q. Mei use Weblogs, which are relative long. 

== Other Discussions ==

It would be interesting to apply the methods used in Jacob et al. to the problems that Q. Mei et al. try to address since LDA claims that it is better than pLSI. 

== Other Questions ==
#How much time did you spend reading the (new, non-wikified) paper you summarized? 2.5 hours
#How much time did you spend reading the old wikified paper? 4 hours
#How much time did you spend reading the summary of the old paper? 10 min
#How much time did you spend reading background material? 3 hours
#Was there a study plan for the old paper? Yes
##if so, did you read any of the items suggested by the study plan? and how much time did you spend with reading them? 3 hours
#Give us any additional feedback you might have about this assignment.
I like this task very much, it helps me to think deeper about the paper I read. It would be great if we can read some paper that have great impact and real applications. The experiments on the paper I read are quite restrict and hard to re-implement.