Fake Reviews Detection through Ensemble Learning
This work addresses the issue of deceptive information for online consumers and platforms, but it is incremental as it builds on existing ensemble learning trends.
The paper tackles the problem of detecting fake online reviews by evaluating ensemble learning approaches, finding that they outperform conventional machine learning algorithms on a dataset of fake restaurant reviews.
Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently, there have been studies reporting the performance of ensemble learning-based approaches in comparison to conventional machine learning techniques. Motivated by the recent trends in ensemble learning, this paper evaluates the performance of ensemble learning-based approaches to identify bogus online information. The application of a number of ensemble learning-based approaches to a collection of fake restaurant reviews that we developed show that these ensemble learning-based approaches detect deceptive information better than conventional machine learning algorithms.