Hunter or Prey? Exploring the Situational Profiles that Define Repeated Online Harassment Victims and Offenders

ABSTRACT

Data collected from a sample of Spanish non-university students (N = 4174) were used to identify unique situational profiles of self-identified repeated online harassment victims and offenders, through a Conjunctive Analysis of Case Configurations (CACC). Repeat victim and offender profiles were constructed using individual-level factors and variables related to the cyber “places” where students go online and their personal information they share while there. Clustering analysis demonstrates that students spent their time online in few situational contexts where online harassment occurs. Dominant situational profiles of students are then provided, along with their associated probabilities for experiencing repeat victimization or committing repeat offending, identifying those at relatively higher and lower risk. Results show that composite profiles associated with victims of repeated online harassment are dissimilar to those associated with offenders of repeated online harassment, suggesting that each form of online harassment occurs in different situational contexts and therefore requires different preventative measures. Our findings are discussed in terms of criminological theory, future online harassment research, cybercrime prevention, and policy implications.

Acknowledgments

This work was supported by the Spanish Ministry of Science, Innovation and Universities under Grant FPU16/01671; and by the Governing Council of Castile-Leon under Grant A2017/009386.

Notes

¹ Personal communication.

² See Miethe, Hart, and Regoeczi (2008), Hart (2014), and Hart, Rennison, and Miethe (2017) for a discussion on the decision rules for defining dominant profiles.

³ A chi-square goodness-of-fit test is used to determine whether data from an existing data file cluster among dominant case configurations more than expected and Hart’s (2019) Situational Clustering Index (SCI) is used to measure the magnitude of clustering if it is detected. The SCI is a standardized metric, similar to the Gini coefficient.

Additional information

Notes on contributors

Asier Moneva

Asier Moneva is research personnel in training (FPU16/01671) at Crímina Research Center for the Study and Prevention of Crime at Miguel Hernandez University of Elche. Asier is a member of the Cybercrime Working Group of the European Society of Criminology. His research interests include cybercrime analysis and prevention from a situational perspective.

Fernando Miró-Llinares

Fernando Miró-Llinares is Professor of Criminal Law and Criminology at Miguel Hernandez University of Elche and Chair of the Crímina Research Center for the Study and Prevention of Crime. Fernando is a board member of the Cybercrime Working Group of the European Society of Criminology. His research interests include cybercrime, artificial intelligence in criminal justice, ethics, and crime prevention.

Timothy C. Hart

Timothy C. Hart is Assistant Professor at University of Tampa and Adjunct Member of the Griffith Criminology Institute at Griffith University. His research interests include applied statistics and quantitative methodologies, victimization, and spatiotemporal crime patterns.