South African Crime Quarterly 2024 contains an article by Manganyi et al. that outlines the lessons learnt during data collection with the South African Police Service for the third national femicide study during the Covid-19 pandemic. The paper discusses the intricacies of collecting data with SAPS and suggests ways to improve data collection and minimise the service disruption on SAPS of research impact with the police.
Hraklis Papageorgiou, Joseph Baggott and Martin Bekker examine data gathered during the #FeesMustFall movement, using a machine learning model trained on historical South African Twitter data combined with event records from a protest database. They find that these events establish a distinctive time-series pattern, suggesting a robust approach to modelling, identifying or predicting protests within South Africa. They argue that automated protest event analysis may soon provide valuable information about the actors, level of turmoil, size, duration, grievances, and motivations of protest.
SACQ is published in partnership with the Centre for Criminology at the University of Cape Town. To access individual articles, refer to the table of contents below.
Table of contents - SACQ 73
Editorial
The case of the third national femicide study: Lessons learnt from undertaking research with SAPS during Covid
by Tirhani Manganyi, Asiphe Ketelo, Tholsie Gounden, Mpumelelo Mabhida, Thobeka Majola, Tarique Variava, Shibe Mhlongo, Naeemah Abrahams, Bianca Dekel
Protest events have a 'Twitter Signature': Evidence from South Africa's #FeesMustFall
by Hraklis Papageorgiou, Joseph Baggott, Martin Bekker
Recent prison protests/ riots in South Africa reimagined with different outcomes: A conflict management perspective
by Casper Lӧtter, Gavin Bradshaw
