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Studies have shown that Twitter can be used for health surveillance, and personal experience tweets (PETs) are an important source of information for health surveillance. To mine Twitter data requires a relatively balanced corpus and it is challenging to construct such a corpus due to the labor-intensive annotation tasks of large data sets. We developed a bootstrap method of finding PETs with the use of the machine learning-based filter. Through a few iterations, our approach can efficientlydoi:10.18653/v1/w16-2917 dblp:conf/bionlp/JiangCG16 fatcat:k7njlnekkfgu3cobdy3p4qafxm