Online detection and infographic explanation of spam reviews with data drift adaptation
<|reference_start|>Online detection and infographic explanation of spam reviews with data drift adaptation: 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.<|reference_end|>
@article{de arriba-pérez2024online,
    title={Online detection and infographic explanation of spam reviews with data
  drift adaptation},
    author={Francisco de Arriba-P'erez, Silvia Garc'ia-M'endez, F'atima Leal,
  Benedita Malheiro, J. C. Burguillo},
    journal={Francisco de Arriba-P\'erez, Silvia Garc\'ia-M\'endez, F\'atima
  Leal, Benedita Malheiro, Juan C. Burguillo, Online Detection and Infographic
  Explanation of Spam Reviews with Data Drift Adaptation, Informatica(2024),
  1-25},
    year={2024},
    doi={10.15388/24-INFOR562},
    archivePrefix={arXiv},
    eprint={2406.15038},
    primaryClass={cs.LG cs.AI cs.CL cs.SI}
}