A Novel Evolving Sentimental Bag-of-Words Approach for Feature Extraction to Detect Misinformation

Abstract

The state-of-the-art misinformation detection techniques mainly focus on static datasets. However, a massive amount of information is generated online and the websites are flooded with this legitimate information and misinformation. It is difficult to keep track of this changing information and provide up-to-date accurate status of webpages giving either legitimate information or misinformation. Therefore, to keep the features up-to-date, authors have proposed evolving sentimental Bag-of-Words approach. This involves, updating sentimental features every time the new or changed web contents are read. This process accumulates the sentimental features at different time intervals that can be utilized to detect misinformation in URLs and upgrade the status of the webpage with timely information. Apart from sentimental features, other state-of-the-art features viz. syntactical, Part-Of-Speech Tagging (POST), and TermFrequency (TF) are updated in a timely manner and utilized to detect misinformation. The model performed well with the support vector machine showing an accuracy of 80% while the decision tree classifier showed less accuracy of 56.66%.