A Hybrid TF-IDF and N-Grams Based Feature Extraction Approach for Accurate Detection of Fake News on Twitter Data (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
As there is an exponential growth of social networks and due to large usage of social media,there is an increasing demand for data in the web for the users which leads to current inclinationsconcepts in the area of research. Sentiment, text analysis and social media analysis, especially inuser reviews and a tweet has become a popular area of research. Fake and Real data classificationfrom user responses plays a key role in fake detection on social media platforms. Fake news andlack of trust in the media are growing problems with huge difficulties in our society. Evidently ina deceptive story or fake news in social Medias leads to change its description. The mainobjective of this research is to detect the fake news, which is a classic text classification problemwith a straightforward proposition. There is needed to build a model that can differentiatebetween “Real” news and “Fake” news with NLP (Natural Language Processing) and ML(Machine Learning) techniques for discovering the 'fake news', or deceptive news stories thatarises from the defective bases. Often, some preprocessing steps and feature extractiontechniques are applied to obtain features from twitter data to improve the accuracy usingsupervised classification algorithm. This paper elucidates the ML classification approaches withdifferent feature extraction techniques to obtain a text analysis and the results obtained arecompared to identify the best possible approach.