Online detection and infographic explanation of spam reviews with data drift adaptation

TL;DR

This paper presents an online, adaptive, and explainable system for detecting spam reviews in data streams, combining machine learning, data drift handling, and visual explanations to improve transparency and accuracy.

Contribution

It introduces a novel online framework that integrates data drift adaptation with explainable spam review detection, addressing transparency and real-time performance.

Findings

Achieved up to 87% spam F-measure

Integrated data drift detection with explainable ML models

Provided a visual dashboard for review explanations

Abstract

Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87 % spam F-measure.