"Comparative Study of Machine Learning Algorithms for Fake Review Detection with Emphasis on SVM"

Online reviews have become an essential factor in
consumer decision-making, with the credibility and authenticity
of such reviews being a major concern. Fake reviews, including
those generated by computers and humans, can significantly
influence the opinions and decisions of consumers, resulting in a
loss of trust in online platforms. The e-commerce sector has seen
a rise in the prevalence of fake reviews, with some sellers
engaging in deceptive practices to manipulate the ratings and
rankings of their products. One such practice is creating fake
positive reviews for their own products or paying individuals to
do so. This can mislead customers into believing that the
products are of high quality and popular when they are subpar.
Another practice involves leaving fake negative reviews for a
competitor's products to damage their reputation and gain a
competitive advantage. In addition, some sellers offer discounts
or incentives to customers in exchange for positive reviews,
leading to biased and inaccurate assessments of the quality of
their products. These practices can harm the sales of honest
sellers and undermine the trust of consumers in the e-commerce
marketplace. This paper proposes a supervised machine learning
approach to identify fake reviews. The study compares the
performance of six classification algorithms, namely Logistic
Regression, K Nearest Neighbours, Support Vector Classifier,
Decision Tree Classifier, Random Forests Classifier, and
Multinomial Naive Bayes. The models are trained on a text
dataset of 40433 reviews collected from https://osf.io/. The paper
analyses the various features and techniques used in the different
algorithms to detect fake reviews. The study concludes that
supervised machine learning algorithms can effectively detect
fake reviews and can be used to prevent their dissemination, thus
enhancing the credibility and reliability of online reviews.