Challenges in Extracting Forensic Data on Sexual and Gender-Based Violence Victims

Sexual violence is one of the most long-standing forms of gender-based violence. It constitutes a violation of human, sexual, and reproductive rights and should be recognized as a public health issue. A key challenge and necessity for public safety is identifying the profiles of victims of sexual and gender-based violence to implement effective measures to combat this type of violence. While Artificial Intelligence has the potential to aid in automated identification, its effectiveness is hindered by a lack of structured data on the subject. Thus, this study aims to present a method for extracting and analyzing textual information found in reports of sexual and gender-based violence concerning victims examined by forensic institutions.

Institutions

Publication Details

Journal
Journal of the Association for Information Systems
Published
2025-08-14
Primary Topic
Authorship Attribution and Profiling
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Challenges in Extracting Forensic Data on Sexual and Gender-Based Violence Victims

Journal of the Association for Information Systems
Authorship Attribution and Profiling
article

Challenges in Extracting Forensic Data on Sexual and Gender-Based Violence Victims

article en

Abstract

Sexual violence is one of the most long-standing forms of gender-based violence. It constitutes a violation of human, sexual, and reproductive rights and should be recognized as a public health issue. A key challenge and necessity for public safety is identifying the profiles of victims of sexual and gender-based violence to implement effective measures to combat this type of violence. While Artificial Intelligence has the potential to aid in automated identification, its effectiveness is hindered by a lack of structured data on the subject. Thus, this study aims to present a method for extracting and analyzing textual information found in reports of sexual and gender-based violence concerning victims examined by forensic institutions.

Journal of the Association for Information Systems
Universidade Federal de Mato Grosso (BR)
Universidade Federal de Mato Grosso do Sul
Gender equality
Openalex Percentile: Top 72%
Authorship Attribution and Profiling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.