BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Tailored Health Messages: Customizing Communication with Computer Technology
Tailored Health Messages: Customizing Communication with Computer Technology
Answering complex questions with random walk models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Using natural language processing to classify suicide notes
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Language use as a reflection of socialization in online communities
LSM '11 Proceedings of the Workshop on Languages in Social Media
Detecting distressed and non-distressed affect states in short forum texts
LSM '12 Proceedings of the Second Workshop on Language in Social Media
One size does not fit all: multi-granularity search of web forums
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Forums and mailing lists dedicated to particular diseases are increasingly popular online. Automatically inferring the health status of a patient can be useful for both forum users and health researchers who study patients' online behaviors. In this paper, we focus on breast cancer forums and present a method to predict the stage of patients' cancers from their online discourse. We show that what the patients talk about (content-based features) and whom they interact with (social network-based features) provide complementary cues to predicting cancer stage and can be leveraged for better prediction. Our methods are extendable and can be applied to other tasks of acquiring contextual information about online health forum participants.