Showing posts with label Social Networks. Show all posts
Showing posts with label Social Networks. Show all posts
Saturday, February 25, 2012
Mining Twitter Article (Not technical)
This is an interesting article http://isc.sans.edu/diary.html?storyid=5728 due to the fact that they have links that are interesting related to mining Twitter. It is a little outdated and a few of the links do not work but still semi-useful. Keep in mind data sets that were previously available, for example http://snap.stanford.edu/data/twitter7.html, are no longer available due to a Twitter request (read about it here). However you can still use the Twitter API to get data, you are just limited to the number of tweets you can get per day/session (can't remember).
Also useful is this article.
Paper Summary - Short text classification in twitter to improve information filtering
Short text classification in twitter to improve information filtering, B. Sriram and D. Fuhry and E. Demir and H. Ferhatosmanoglu,2010
This paper describes research that classifies tweets using a reduced set of features. In this approach they try to classify text into the following set of classes "News, Events, Opinions, Deals, and Private Messages". The problem they present is the curse of dimensionality problem that results from trying to conquer the spareness issue related to classifying twitter messages. Other research typically uses external knowledge bases to support tweet classification. They argue this can be slow due to the need to excessively query the external knowledge base. Important points about this paper:
1. They provide a very useful discussion of Twitter and tweets
2. How they classify tweets is interesting worth another review
This paper describes research that classifies tweets using a reduced set of features. In this approach they try to classify text into the following set of classes "News, Events, Opinions, Deals, and Private Messages". The problem they present is the curse of dimensionality problem that results from trying to conquer the spareness issue related to classifying twitter messages. Other research typically uses external knowledge bases to support tweet classification. They argue this can be slow due to the need to excessively query the external knowledge base. Important points about this paper:
1. They provide a very useful discussion of Twitter and tweets
2. How they classify tweets is interesting worth another review
Paper Summary - Linking Social Networks on the Web with FOAF: A Semantic Web Case Study
Linking Social Networks on the Web with FOAF: A Semantic Web Case Study,J. Golbeck and M. Rothstein, 2008
Another paper related to FOAF and the Semantic Web. This paper examines linking FOAF identities across social networks. They propose that it would be better to merge these accounts across sites so a user has one social network. This may have been an interesting idea in 2008 but I'm not sure if this is relevant now seeing as some social networks let you bring in contacts from other sites. In general, this paper can be excluded from further analysis.
Another paper related to FOAF and the Semantic Web. This paper examines linking FOAF identities across social networks. They propose that it would be better to merge these accounts across sites so a user has one social network. This may have been an interesting idea in 2008 but I'm not sure if this is relevant now seeing as some social networks let you bring in contacts from other sites. In general, this paper can be excluded from further analysis.
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