# Improving Opinion Spam Detection by Cumulative Relative Frequency Distribution

arXiv: https://arxiv.org/abs/2012.13905 · HTML: https://mlsanity.com/paper/2012.13905/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Evaluating the Performance of ChatGPT for Spam Email Detection](https://mlsanity.com/paper/2402.15537/) (2024)
- [Confounds and Overestimations in Fake Review Detection: Experimentally Controlling for Product-Ownership and Data-Origin](https://mlsanity.com/paper/2110.15130/) (2021)

## Abstract (arXiv)

Over the last years, online reviews became very important since they can influence the purchase decision of consumers and the reputation of businesses, therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them, by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.