Automatically Assessing Quality of Online Health Articles

Today Information in the world wide web is overwhelmed by unprecedented quantity of data on versatile topics with varied quality. However, quality information disseminated field medicine has been questioned as negative health consequences misinformation can be life-threatening. There currently no generic automated tool for evaluating online spanned over broad range. To address this gap, paper, we applied mining approach to automatically assess articles based 10 criteria. We have prepared a labelled dataset 53012 features and different feature selection methods identify best subset which our trained classifier achieved an accuracy $\text{84}\%-\text{90}\%$ Our semantic analysis shows underpinning associations between selected & assessment criteria further rationalize approach. findings will help identifying high thus aiding users shaping their opinion make right choice while picking related from online.