Detecting online child grooming conversation

Massive proliferation of social media has opened possibilities for perpetrator to conduct the crime of online child grooming. Because the pervasiveness of the problem scale, it may only be tamed effectively and efficiently by using an automatic grooming conversation detection system. Previously, Pranoto, Gunawan, and Soewito [1] had developed a logistic model for the purpose and the model was able to achieve 95% detection accuracy. The current study intends to address the issue by using Support Vector Machine and k-nearest neighbors classifiers. In addition, the study also proposes a low-computational cost classification method on the basis of the number of the existing grooming conversation characteristics. All proposed methods are evaluated using 150 conversation texts of which 105 texts are grooming and 45 texts are non-grooming. We identify that grooming conversations possess 17 features of grooming characteristics. The results suggest that the SVM and k-NN are able to identify grooming conversations at 98.6% and 97.8% of the level of accuracy. Meanwhile, the proposed simple method has 96.8% accuracy. The empirical study also suggests that two among the seventeen characteristics are insignificant for the classification.