Dynamic Editing Distance-based Extracting Relevant Information Approach from Social Networks

Online social networks, such as Facebook, Twitter, LinkedIn, etc., have grown exponentially in recent times with a large amount of information. These networks huge volumes data especially structured, textual, and unstructured forms which often led to cyber-crimes like cyber terrorism, bullying, extracting information from these has now become serious challenge order ensure the safety. In this work, we propose new, supervised approach for Information Extraction (IE) Web resources based on remote dynamic editing, called EIDED. Our is part family IE approaches masks extraction articulated around three algorithms: (i) labeling algorithm, (ii) learning inference (iii) an extended edit distance algorithm. proposed able work even presence anomalies tuples missing attributes, multivalued permutation structure web pages. The experimental study, conducted, standard database pages, shows performance our EIDED compared classic distance, respect metrics recall coefficient, precision, F1-measure.