Dissuading internet users from viewing adult-minor sex images: the results of a randomized controlled experiment

A/Prof. Jeremy Prichard1, Dr Caroline Spiranovic1, Prof  Paul Watters2, Prof Richard Wortley3, A/Prof Tony Krone4
1Law Faculty, University Of Tasmania, Hobart, Australia, 2La Trobe University , Melbourne, Australia, 3University College London , London, UK, 4University of Canberra, Canberra, Australia

The task of tackling the market in child exploitation material (CEM) is almost entirely undertaken by law enforcement agencies (LEA). Automated online warning messages are recognised as one cost effective primary prevention method to reduce the numbers of new CEM users. But their use remains in abeyance because no clear evidence base has existed as to the effect of automated messages on users.

This presentation discusses the results of an online double-blind randomised controlled experiment. The study established a real-life male-oriented website and used commercial strategies to draw web traffic. Users who clicked on a fake advertisement for ‘barely legal porn’ (BLP) were randomly allocated to: a control group (who did not receive an automated message); or one of four experimental groups (who received messages relating to law enforcement or harm). Users’ behaviour was monitored to assess whether they attempted to access the BLP. (BLP was used as a proxy for CEM because attempting to access a fake advert for CEM could constitute an offence.)

The results showed those who received a law enforcement message were less likely than the control group to attempt to access the BLP.

The presentation will discuss what the effect of the messages might look like in a real-life scenario involving actual CEM. The implications of the findings will be considered for a variety of non-LEA agencies that could employ automated messages at the local, national and international level.


Biography:

A/Prof Jeremy Prichard teaches Criminal Law as well as Sex Crimes and Criminals. He works in two interdisciplinary teams – one on child exploitation material and the other on illicit drug markets. Both teams explore novel ways to conduct empirical research to inform policy and practice.

Caroline Spiranovic is a Senior Research Fellow at the Faculty of Law, University of Tasmania and an Honorary Research Fellow, School of Population Health, Faculty of Medicine, Dentistry and Health Sciences, University of Western Australia. Caroline works predominantly in multi-disciplinary teams on criminology research projects focusing on public opinion, crime prevention and sex offending.

“Drugs? Online? Naaaah surely not”: perceptions of risk and reward amongst darknet drug vendors

A/Prof. James Martin1
1Swinburne University, Hawthorn, Australia

The encrypted darknet is increasingly used as a means by which people buy and sell illicit drugs. This trend is particularly evident in Australia, which has the 2nd highest concentration of online drug vendors per capita of any country. Despite the growing proportion of the global illicit drugs trade that is conducted via the darknet, very little is known about those who use these advanced digital technologies to sell illicit drugs.

This research is intended to help fill this critical gap in knowledge by engaging directly with people who use the darknet to sell illicit drugs. Using data gathered from interviews with online drug vendors conducted through encrypted chat applications, this paper will explore the perceptions of risk and reward of those who sell drugs on the darknet, and how those involved in online drug trading perceive other differences with the conventional street-based drugs trade.


Biography:

Associate Professor James Martin is Criminology Convener at Swinburne University, and is one of the leading researchers working in the area of cryptomarkets and online drug trading. His book, ‘Drugs on the Darknet: How Cryptomarkets are Transforming the Global Trade in Illicit Drugs’ was the first research monograph published in the world on this topic, and was cited in the trial of Ross Ulbricht, administrator of the infamous cryptomarket, Silk Road.

“I’ve never had to go down this path before”: Applicant experiences of an online family violence intervention order process.

Prof. Stuart Ross1, Ms. Sophie Aitken2
1School Of Social & Political Sciences, University Of Melbourne, Northcote, Australia, 2Caraniche, 1/260 Hoddle Street, Abbotsford, Australia

Domestic violence protection orders are civil orders intended to protect victims from further violence. However their availability to victims and ultimately their effectiveness is limited by complex procedural requirements and court accessibility barriers. One solution has been to transfer responsibility for initiating applications to police, but this reduces victims’ control over the process, and can result in inadequate information collection and outcomes that are inconsistent with victim preferences. An evaluation of an online Family Violence Intervention Order application process trialed in three courts in Victoria, Australia showed that it simplifies the application process, enhances applicant agency and reduces stress, speeds up case processing and reduces the workload of court staff. The success of this initiative raises wider questions about the importance of participant agency and control in the family violence system.


Biography:

Stuart Ross is Enterprise Professor in Criminology in the School of Social & Political Sciences at the University of Melbourne and sits on the Master of Criminology Advisory Board. The Enterprise Professor role was created to enhance the links between the University of Melbourne and industry. In this capacity, Stuart has provided consultancy research and evaluation services to a range of State and Commonwealth agencies, both directly and in partnership with other universities and consulting firms. Prior to joining Criminology at the University of Melbourne, he was Director of the National Centre for Crime and Justice Statistics in the Australian Bureau of Statistics.

Sophie Aitken is Manager, Program Development for Caraniche, a provider of forensic psychology programs in the youth and adult justice sector. She has oversight of program development across the full range of the company’s forensic and general psychology services.

Predicting Crime Rates Using Demographic Data and Features Derived From Social Media

Mr Tony Moriarty1, Mr Richard Nichol1, Mr Praveen Kumar1, Mr Chao Sun2, Dr Roman Marchant3
1The University of Sydney, Sydney, Australia, 2Sydney Informatics Hub, The University of Sydney | Faculty of Arts and Social Science, The University of Sydney, Sydney, Australia, 3Centre for Translational Data Science, The University of Sydney, Sydney, Australia

Social media is a recent phenomenon whose usage pattern is constantly evolving, presenting an interesting challenge and an opportunity to enhance the analysis of patterns of crime. Social media features that may be predictive of crime rate include those derived directly from the text used in social media posts, and text-independent features derived from metadata or data aggregations which may indicate transient and shifting population characteristics not captured in static demographic statistics.

In this study we examine associations between social media and crime and attempt to predict the rates of certain categories of crime in NSW local government areas (LGAs).

We use Natural Language Processing techniques to analyse the Tracking Infrastructure for Social Media Analysis (TRISMA) historical twitter data from 2016, focusing on aggregating tweets by 130 LGAs. Augmenting the models with demographic information obtained from the Australian Bureau of Statistics 2016 Census, we model our target data, crime statistics for 6 crimes by LGA (2016); from NSW Bureau of Crime Statistics and Research (BOCSAR, 2018).

We find that a spatial model based entirely on Twitter data is predictive of crime rate across all 6 crime categories we analyse. We further find evidence that Twitter derived features may be used to enhance the accuracy of crime rate predictions for some crime categories through an ensemble result which improves on a demographic model in 4 of 6 crimes of which 2 are significant.


Biography:

From fraud detection to predicting customer behaviour, Tony Moriarty worked on these projects and more in his former life as a Machine Learning Engineer at a Fortune 500. During this time he devised a system for ranking job candidates, as well as using behavioural analysis and outlier detection to identify employees stealing intellectual property. His Masters of Data Science thesis shifted his focus more broadly to quantitative criminology.

He is currently co-founder of a startup performing data mining in the real estate sector.

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