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Adverse reactions caused by drugs following their release into the market areamong the leading causes of death in many countries. The rapid growth ofelectronically available health related information, and the ability to processlarge volumes of them automatically, using natural language processing (NLP)and machine learning algorithms, have opened new opportunities forpharmacovigilance. Survey found that more than 70% of US Internet users consultthe Internet when they require medical information. In recent years, researchin this area has addressed for Adverse Drug Reaction (ADR) pharmacovigilanceusing social media, mainly Twitter and medical forums and websites. This paperwill show the information which can be collected from a variety of Internetdata sources and search engines, mainly Google Trends and Google Correlate.While considering the case study of two popular Major depressive Disorder (MDD)drugs, Duloxetine and Venlafaxine, we will provide a comparative analysis fortheir reactions using publicly-available alternative data sources.

Mining the Web for Pharmacovigilance: the Case Study of Duloxetine and Venlafaxine