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Online sexual grooming and offender tactics -: What can we learn from social media dialogues?
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Media, Technology and Culture och Interaction Lab)
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Media, Technology and Culture och Interaction Lab)
2015 (English)In: Proceedings of the 2015 Swecog Conference / [ed] Billing, E., Lindblom, J. & Ziemke, T., Skövde, 2015, Vol. 3, 23-23 p.Conference paper, Poster (Refereed)
Abstract [en]

While online social networking sites and other digital media provide a means for positive online experiences, they are also being misused for offences like online sexual grooming. Attempts have been made to analyse and model online grooming in order to understand this kind of predator behaviour (e.g., O’Connell, 2004; Williams et al., 2013). This research, and the resulting models of the grooming process, is however, invariably based on material where adult decoys (e.g., researchers, law enforcement officers, adults trained to entrap offenders) pose as children in the interaction with potential offenders. We argue that such material, i.e., decoy-offender chat logs, does not reflect real grooming processes; Decoys have an underlying agenda to make prosecutable cases against offenders, which entails decoys resorting to manipulation tactics otherwise typical for offender behaviour. In all essence, this often leads to a dialogue with two adults using grooming tactics on each other, and the resulting models do not capture the patterns of child-offender dialogues.

Contrary to previous research, we have analysed real-world child-offender chat logs from closed forums. Our data set, selected dialogues (ca. 500 pages) from a corpus of ca. 12 000 A4-pages was thematically analysed and categorised using NVivo 10 software. The coding was done by both authors for inter-rater reliability. Where coding differed, the authors explored the categorisation until agreement was reached (cf., Whittle et al., 2013). The material was also compared to decoy-offender chat logs (ca. 100 pages, publically available on

The analysis of the different data sets reveal quite different pictures of the grooming process. While previous models describe the grooming process as sequential (O’Connell, 2004) or thematic (Williams et al., 2013), our findings suggest a far more complex behavioural pattern – significantly diverse dialogue patterns with different tactics emerge, depending on whether the respondent is a decoy or a child, and their respective responses. The (preliminary) results show differences in both dialogue and process structure. Dialogues with decoys commonly show what can best be described as “artificial compliance”, presumably due to their underlying agenda of generating prosecutable cases. Furthermore, decoys tease out personal information from the offenders, and also share “personal” information about themselves, even when not asked for it.

Child-offender dialogues instead show patterns of reluctance or objections to offender requests for personal information, suggestions of sexual nature, etc. Another offender tactic is threats to obtain compliance, which was not found in any of the analysed decoy-offender dialogues. Other deviations include differences in dialogue length, number of dialogue turns, and complexity, with regard to changes in topics and offender tactics. Further research is necessary for a more thorough understanding of online grooming, and new models are needed that reflect real-world grooming processes. This includes offender behaviours, reasoning, decisions, and tactics used in grooming. Further, such knowledge is of outmost importance for risk awareness measures for young people so they can better cope with online challenges and risks, and make sensible judgements and decisions in online interactions.

Place, publisher, year, edition, pages
Skövde, 2015. Vol. 3, 23-23 p.
, Skövde University Studies in Informatics, ISSN 1653-2325 ; 2015:3
National Category
Interaction Technologies
URN: urn:nbn:se:his:diva-11628ISBN: 978-91-978513-8-1OAI: diva2:864288
2015 Swecog Conference, The Swedish Cognitive Science Society
Available from: 2015-10-26 Created: 2015-10-26 Last updated: 2015-12-15Bibliographically approved

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