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Detecting Sexual Predatory Chats by Perturbed Data and Balanced Ensembles Effects

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dc.contributor.authorBorj, Parisa Rezaee
dc.contributor.authorRaja, Kiran
dc.contributor.authorBours, Patrick
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDamer, Naser
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana
dc.contributor.editorUhl, Andreas
dc.date.accessioned2021-10-04T08:43:48Z
dc.date.available2021-10-04T08:43:48Z
dc.date.issued2021
dc.description.abstractSecuring the safety of the children on online platforms is critical to avoid the mishaps of them being abused for sexual favors, which usually happens through predatory conversations. A number of approaches have been proposed to analyze the content of the messages to identify predatory conversations. However, due to the non-availability of large-scale predatory data, the stateof-the-art works employ a standard dataset that has less than 10% predatory conversations. Dealing with such heavy class imbalance is a challenge to devise reliable predatory detection approaches. We present a new approach for dealing with class imbalance using a hybrid sampling and class re-distribution to obtain an augmented dataset. To further improve the diversity of classifiers and features in the ensembles, we also propose to perturb the data along with augmentation in an iterative manner. Through a set of experiments, we demonstrate an improvement of 3% over the best stateof-the-art approach and results in an F1-score of 0.99 and an Fβ of 0.94 from the proposed approach.en
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37461
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-324
dc.subjectImbalanced dataset
dc.subjectOnline Conversation
dc.subjectPredatory Detection
dc.subjectSampling
dc.titleDetecting Sexual Predatory Chats by Perturbed Data and Balanced Ensembles Effectsen
dc.typeText/Conference Paper
gi.citation.endPage252
gi.citation.publisherPlaceBonn
gi.citation.startPage245
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleFurther Conference Contributions

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P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group