Feed Forward Neural Network based Effective Feature Extraction technique for better Classification accuracy

Abstract

Discovery of trustworthy information from so- cial media (i.e. Facebook, Twitter, and Instagram) is one of the crucial and challenging tasks in data processing. With the advent of internet and social network usage large chunks of dark data is being generated. Nevertheless, In- sights are generated and decision are taken by organiza- tions using this data. So the most important question is how reliable is your data and equally how reliable are the users who generate the data. Twitter is a most famous micro blogging social media. The earlier analysis shows that the maximum information which are being tweeted via twitter is mostly assumed to be true in form and there are no checks that have been carried out for its creden- tials. But the spread of misinformation dynamics makes  it even more crucial to ascertain the trustworthiness of the data from a humanitarian perspective. Thus, using an auto- matic and effective Feature Extraction technique and Feed forward neural network classification the tweets and the twitter users are classified as either True or False and re- liable or Unreliable respectively. The case study is exper- imented on a real- world data set which demonstrates the effectiveness of the proposed approach while comparing with the existing truth discovery methods.