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

Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, J. C. Burguillo
6/21/2024
cs.LGcs.AIcs.CL

No official code found. Best implementation: nlpgti/data_drift (0 stars, Apache-2.0, last commit 2024-05-30).

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.

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Code Implementations(1)

nlpgti/data_driftCommunity100%
Apache-2.0
00PythonMay 29, 20242 years agoApache-2.0

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Cite this paper

@article{arribaprez2024online,
  title  = {Online detection and infographic explanation of spam reviews with data drift adaptation},
  author = {Francisco de Arriba-Pérez and Silvia García-Méndez and Fátima Leal and Benedita Malheiro and J. C. Burguillo},
  year   = {2024},
  eprint = {2406.15038},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.15038v1}
}

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