Suchergebnisse

This research explores hacktivism as a new form of online political activism. It uses qualitative interviews with a gender-equal sample of 10 self-defined hacktivists to address issues of gender and the discursive strategies used by males and females to handle the hacktivist community's male-only stereotype. The semi-structured interviews are analysed using Foucauldian discourse analysis (FDA). The analysis indicates that male hacktivists relate to this dominant male-only representation through discursive techniques such as the suppression of gender (Male Oblivious Discourse) or mechanisms of vindication (Male Justification Discourse). Female hacktivists use the accentuation of gender and sexism to counteract male-dominant discourses and establish Female Discourses of Resistance (Emphasis Discourse; Negation Discourse). These gender-related argumentative positions and rhetorical mechanisms demonstrate how the male-only stereotype is created and maintained and how it affects not only hacktivists' talk and sense-making but also their identity and the hacktivist actions they perform.

AbstractOngoing efforts by state actors to collaborate on addressing the challenges of global cybersecurity have been slow to yield results. Technical expert communities such as Computer Security and Incident Response Teams (CSIRTs) have played a fundamental role in maintaining the Internet's functional structure through transnational collaboration. Responsible for security incident management and located in diverse constituencies, these coordination centres engage in joint responses and solve day‐to‐day cybersecurity problems through diverse national, regional and international networks. This article argues that CSIRTs form an epistemic community that engages in science diplomacy, at times navigating geopolitical tensions in a way that political actors are not able to. Through interviews with CSIRT representatives, we explain how their collaborative actions, rooted in shared technical knowledge, norms and best practices, contribute to the advancement of international cooperation on cybersecurity.

Abstract
Purpose
Technology-facilitated domestic abuse (TFDA) is a prevalent form of domestic abuse. While police are recognized as critical first responders to intimate partner violence, there is limited research about what the challenges to policing TFDA are perceived to be and how they might be addressed. This article speaks to this issue.

Methods
Between April 2020 and June 2022, n = 196 Australian and n = 256 United Kingdom representatives of services engaged with domestic abuse victim-survivors participated in an online survey about TFDA. Survey components asked respondents to report on challenges to policing TFDA. The authors analyzed these comments.

Results
Key themes identified from the survey responses regarding challenges to policing TFDA include that participants held perceptions that (a) police do not recognize TFDA as an aspect of coercive control and thus do not recognize its seriousness, (b) police receive inadequate training about TFDA, (c) police have insufficient time and personnel to tackle TFDA and (d) evidence collection is an obstacle to policing TFDA.

Conclusions
The study points to a need to address the perceived concerns associated with policing TFDA to effectively respond to domestic abuse in the digital age and ensure domestic abuse perpetrators who misuse emerging technologies are held accountable.

Technology-facilitated abuse or 'tech abuse' in intimate partner violence (IPV) contexts describes the breadth of harms that can be enacted using digital systems and online tools. While the misappropriation of technologies in the context of IPV has been subject to prior research, a dedicated study on the United Kingdom's IPV support sector has so far been missing. The present analysis summarises insights derived from semi-structured interviews with 34 UK voluntary and statutory sector representatives that were conducted over the course of two years (2018–2020). The analysis identifies four overarching themes that point out support services' practices, concerns and challenges in relation to tech abuse, and specifically the Internet of Things (IoT). These themes include (a) technology-facilitated abuse, where interviewees outline their experiences and understanding of the concept of tech abuse; (b) IoT-enabled tech abuse, focusing on the changing dynamics of tech abuse due to the continuing rise of smart consumer products; (c) data, documentation and assessment, that directs our attention to the shortcomings of existing risk assessment and recording practices; and (d) training, support and assistance, in which participants point to the need for specialist support capabilities to be developed within and beyond existing services.

Abstract
Purpose
Computational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work.

Methods
This article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review.

Results
The included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research.

Conclusions
Text mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches.