SynthesisScientific reports2025
Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review.
Synthesis in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This study presents a systematic review (SR) and meta-analysis (MA) on the use of machine learning (ML) methods for detecting online grooming, a form of manipulation and child sexual abuse. The SR identified 33 studies from IEEE, Web of Science, Scopus, Springer, PubMed, and Google Scholar databases, and 11 ML methods were meta-analyzed for accuracy (ACC), precision (P), recall (R), and [Formula: see text] Score (F1). Multilayer Perceptron (MLP) demonstrated the highest accuracy (ACC=92%, p<0.001) and precision (P=81%, p<0.001), excelling in capturing complex, nonlinear patterns essential for analyzing nuanced online interactions. Support Vector Machine (SVM), with an ACC of 88% (p<0.001), achieved a balanced performance, characterized by high precision (P=86%, p<0.001), recall (R=74%, p<0.001), and the highest F1 score (0.79). SVM emerges as an effective algorithm, providing a robust balance across all metrics, emphasizing its adaptability and reliability in detecting nuanced grooming behaviors. This study is pioneering in meta-analyzing ML methods applied to the effectiveness in detecting grooming. The results highlight the efficacy of certain algorithms and contribute to the identification of online predators. A crucial aspect of cybersecurity for preventing child sexual abuse.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.