Toward Spotting the Pedophile Telling victim from predator in text chats

Pendar, Nick

doi:10.1109/icosc.2007.4338354

Cited by 22 publications

(34 citation statements)

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“…Content may change prior to final publication. Although misspellings can have an important meaning in the context of the chat as argued in [7] [19], we conclude that, at least in our dataset, any potentially meaningful misspellings are dwarfed by the number of superfluous misspellings. As stated before, this is undoubtedly due to the young age of the chat participants in this case.…”

Section: A Bag Of Wordsmentioning

confidence: 55%

“…Pendar [7] defines the types of data for predators as follows There is a general lack of publicly available chatlogs of type 1a because of privacy issues [1], [8], whereas chats of type 1b have been made available through nonprofit, volunteer based organisations, specifically pervertedjustice.com.…”

Section: A Publicly Available Datamentioning

confidence: 99%

“…One of the earliest experiments was conducted in 2007 by Pendar [7]. To discriminate between a predator and victim in a text containing a predatory conversation, he collected 701 text logs from PJ.…”

Section: B Previous Approachesmentioning

confidence: 99%

“…Although only a few of the contributions have been discussed in this section, there is a common denominator amongst them despite different approaches. It is feasible to discriminate between a predator and nonpredator with relative high accuracy using supervised classifiers in combination with simple BoW representation [7], [16], [19], [24]. In addition, high-level, behavioural features have been being incorporated into the feature set [3], [4], [17], [18], [18].…”

Section: B Previous Approachesmentioning

confidence: 99%

“…Bag of Words (BoW) has been established in numerous previous paper as an effective feature for text classification [3], [7], [18], [25]. In all our BoW representations, we employed tf-idf weighting and bigram language model, which are widely accepted and utilized in PAN2012.…”

Section: A Bag Of Wordsmentioning

confidence: 99%

See 4 more Smart Citations

Detecting Predatory Behavior in Game Chats

Cheong

Jensen

Gudnadottir

et al. 2015

IEEE Trans. Comput. Intell. AI Games

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While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStarPlanet, a massively multiplayer online game for children. The research described in this paper aimed to detect predatory behaviours in the chats using machine learning methods. In order to achieve a high accuracy on this task, extensive preprocessing was necessary. We describe three different strategies for data selection and preprocessing, and extensively compare the performance of different learning algorithms on the different datasets and features.

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Section: A Bag Of Wordsmentioning

confidence: 55%

Section: A Publicly Available Datamentioning

confidence: 99%

Section: B Previous Approachesmentioning

confidence: 99%

Section: B Previous Approachesmentioning

confidence: 99%

Section: A Bag Of Wordsmentioning

confidence: 99%

See 3 more Smart Citations

Detecting Predatory Behavior in Game Chats

Cheong

Jensen

Gudnadottir

et al. 2015

IEEE Trans. Comput. Intell. AI Games

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Text Mining and Cybercrime

2010

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Constructing an inter‐post similarity measure to differentiate the psychological stages in offensive chats

Miah

Yearwood

Kulkarni

2014

Asso for Info Science & Tech

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Offensive Internet chats, particularly the child‐exploiting type, tend to follow a documented psychological behavioral pattern. Researchers have identified some important stages in this pattern. The psychological stages broadly include befriending, information exchange, grooming, and approach. Similarities among the posts of a chat play an important role in differentiating as well as in identifying these stages. In this article a novel similarity measure is constructed which gives high Inter‐post‐similarity among the chat‐posts within a particular behavioral stage and low inter‐post‐similarity across different behavioral stages. A psychological stage corpus‐based dictionary is constructed from mining the terms associated with each stage. The dictionary works as a background knowledge‐base to support the similarity measure. To find the inter‐post similarity a modified sentence similarity measure is used. The proposed measure gives improved recognition of inter‐stage and intra‐stage similarity among the chat posts compared with other types of similarity measures. The pairwise inter‐post similarity is used for clustering chat‐posts into the psychological stages. Results of experiments demonstrate that the new clustering method gives better results than some current clustering methods.

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Toward Spotting the Pedophile Telling victim from predator in text chats

Cited by 22 publications

References 0 publications

Detecting Predatory Behavior in Game Chats

Detecting Predatory Behavior in Game Chats

Text Mining and Cybercrime

Constructing an inter‐post similarity measure to differentiate the psychological stages in offensive chats

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