1,765
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Detecting Victim Blaming Biases Using Social Media

, M.Sc & , Ph.D
Pages 436-450 | Published online: 18 May 2020
 

ABSTRACT

In attempts to identify and remove biased individuals from a pool of potential jurors, attorneys have resorted to real-time social media investigations, looking at the opinions and affiliations of candidates. Attorneys’ conclusions are based less on founded research and more on their own personal opinions and common-sense theories. This study investigated the relationship between self-reported Facebook “sharing”/“liking” behavior and victim blaming in a sexual assault scenario. Using an online questionnaire, participants indicated how they would interact with controversial memes and news articles on Facebook, gave a recommended verdict and sentencing length for a sexual assault vignette, as well as completed a rape myth acceptance scale. Logistic and linear regression analyses showed that both gender and liberal sharing behavior were significant predictors of verdict decision and sentencing length. Women were more likely to find a defendant guilty than men, and jurors who share more liberal-leaning posts on Facebook were more likely to give a longer sentencing length. Results suggest that Facebook post-sharing could be a useful gauge of jurors’ attitudes.

Disclosure statement

No potential conflict of interest was reported by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 221.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.