Sextortion is a form of blackmail in which images of the victim nude or engaged in sexual acts are used as leverage by the offender. Typically, the offender will threaten the victim with public exposure of the images. Some offenders go so far as identifying and naming friends, family and coworkers of the victim as targets to whom they will disclose the images, maximising the potential reputational damage from disclosure. Recent work has recognised a range of behaviour to be captured under the definition of sextortion. O’Malley and Holt (2020) derive four distinct categories of sextortion offender from a survey of media and court documents related to sextortion cases: those focused on minors, cybercriminals who obtain blackmail material via computer intrusion, offenders who are former or current intimate partners, and transnational criminals who lure victims into sexual encounters online before then blackmailing them with recordings of these encounters. To these categories we may already add a fifth: sextortion spammers, who have no genuine source of blackmail material at all, but make unevidenced threats via bulk email, and collect ransom via cryptocurrency transactions. Sextortion as a whole is clearly an evolving and rapidly-growing form of cybercriminality which, due to its nature, poses significant investigative hurdles for law enforcement and researchers.
Our project responds to this need by collecting together two major and previously unexamined sources of data on sextortion offending. A dataset already available to our research team consists of over 20,000 anonymised reports recorded internationally over the past decade, and this resource will be augmented by project partners’ contribution of an estimated 1,000 crime reports from the Avon & Somerset area.
The aims of our analysis will be: (1) to fill foundational empirical gaps in current knowledge about sextortion dynamics, including the various methodologies employed in different variants of the crime, the financial losses experienced and the typical characteristics of victims; (2) to identify the major online locations for sextortion offending, both historically and emerging over the past
18 months, which should be targeted for future interventions; (3) to identify disruption opportunities from our analysis of the various sextortion methodologies exposed within our data, and especially areas where automated tools or targeted police action could have an especially protective impact.