Towards identifying drug side effects from social media using active learning and crowd sourcing.

Motivation Social media is a largely untapped source of information on side effects of drugs. Twitter in particular is widely used to report on everyday events and personal ailments. However, labeling this noisy data is a difficult problem because labeled training data is sparse and automatic labeling is error-prone. Crowd sourcing can help in such a scenario to obtain more reliable labels, but is expensive in comparison because workers have to be paid. To remedy this, semi-supervised active learning may reduce the number of labeled data needed and focus the manual labeling process on important information. Results We extracted data from Twitter using the public API. We subsequently use Ama…