A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Web classification using support vector machine
Proceedings of the 4th international workshop on Web information and data management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Effective Methods for Improving Naive Bayes Text Classifiers
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Classification of Web Documents Using a Naive Bayes Method
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Eddi: interactive topic-based browsing of social status streams
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Discovering users' topics of interest on twitter: a first look
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Analyzing cross-system user modeling on the social web
ICWE'11 Proceedings of the 11th international conference on Web engineering
Multinomial naive bayes for text categorization revisited
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Modeling topic specific credibility on twitter
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Finding someone in my social directory whom i do not fully remember or barely know
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
TweetLDA: supervised topic classification and link prediction in Twitter
Proceedings of the 3rd Annual ACM Web Science Conference
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Real-time information streams such as Twitter have become a common way for users to discover new information. For most users this means curating a set of other users to follow. However, at the moment the following granularity of Twitter is restricted to the level of individual users. Our research has highlighted that many following relationships are motivated by a subset of interests that are shared by the users in question. For example, user A might follow user B because of their technology related tweets, but shares little or no interest in their other tweets. As a result, this all-or-nothing following relationship can quickly overwhelm users' timelines with extraneous information. To improve this situation we propose a user profiling approach based on the topical categorisation of users' posted URLs. These topics can then be used to filter information streams so that they focus on more relevant information from the people they follow, based on their core interests. In particular, we present a system called CatStream that provides for a more fine-grained way to follow users on specific topics and filter our timelines accordingly. We present the results of a live-user study that shows how filtered timelines offer a better way to organise and filter their information streams. Most importantly users are generally satisfied with the categories predicted for their profiles and tweets.