C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Machine Learning
ConText: an algorithm for identifying contextual features from clinical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Annotating named entities in Twitter data with crowdsourcing
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Epidemic outbreak and spread detection system based on twitter data
HIS'12 Proceedings of the First international conference on Health Information Science
Real-time spatio-temporal analysis of West Nile virus using Twitter data
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Epidemic intelligence: for the crowd, by the crowd
ICWE'12 Proceedings of the 12th international conference on Web Engineering
Towards large-scale twitter mining for drug-related adverse events
Proceedings of the 2012 international workshop on Smart health and wellbeing
Identifying event-related bursts via social media activities
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Supporting temporal analytics for health-related events in microblogs
Proceedings of the 21st ACM international conference on Information and knowledge management
Automated Twitter data collecting tool for data mining in social network
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Automated Twitter data collecting tool and case study with rule-based analysis
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Extracting event-related information from article updates in wikipedia
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
WiseMarket: a new paradigm for managing wisdom of online social users
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding the diversity of tweets in the time of outbreaks
Proceedings of the 22nd international conference on World Wide Web companion
A framework for detecting public health trends with Twitter
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Aggregating Personal Health Messages for Scalable Comparative Effectiveness Research
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
CUVIM: extracting fresh information from social network
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Comparing the spatial characteristics of corresponding cyber and physical communities: a case study
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Analysis of Microblog Rumors and Correction Texts for Disaster Situations
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.