Abstract:
Analyzing of news truthfulness is a challenging problem in today’s era because
there is a massive information on the social networking sites (SNS) which turns out to
be very difficult to manually analyze. Moreover, the impact of fake or negative news is
tremendously huge to the internet users. In this complex field, scientists use
sophisticated computer algorithms and neural network structures to examine and
distinguish between the truthfulness of textual content that is distributed via various
media channels. As a result, academic research related to filtering and banning fake
news has been highly demanding since very recent years. Although there are some
significant results and improvements made using different feature extraction methods
and classification algorithms it still has some gaps to meet the important necessities to
detect fake news because each method has some biases, variances and generalization
errors. This research contributes to this area by using probabilistic sentiment score and
sentence embedding, marks a significant advance forward in the accuracy of detecting
fake news. It differs significantly from traditional approaches such as TF-IDF or bag-
of-words (BOW) representation, which frequently ignore complex semantic and
contextual nuances. The system first implements probabilistic sentiment model to get
probabilistic sentiment score using TF-IDF, mutual information and logistic regression
methods. Secondly, the system applies sentence embedding method to extract semantic
and contextual feature vectors. The system finally uses Naïve Bayes and Support Vector
Machine classifiers based on concatenated features (Probabilistic sentiment score and
sentence embedding feature vector) for classification process. The system performs the
experiments upon ISOT dataset and other fake news dataset from Kaggle. The
effectiveness of the proposed method is remarkable 99% accuracy rate, which
outperforms other models. Moreover, the results prove that the proposed concatenated
feature is superior not only Naïve Bayes but also Support Vector Machine classifiers.
