A Taxonomy-Driven Case Study of Australian Web Resources Against Technology-Facilitated Abuse

• URL: http://arxiv.org/abs/2512.04104v1
• Date: Fri, 28 Nov 2025 01:02:54 GMT
• Title: A Taxonomy-Driven Case Study of Australian Web Resources Against Technology-Facilitated Abuse
• Abstract summary: Technology-Facilitated Abuse (TFA) encompasses a broad and rapidly evolving set of behaviours in which digital systems are used to harass, monitor, threaten, or control individuals.<n>This paper contributes a unified, literature-derived taxonomy of TFA grounded in a structured review of peer-reviewed studies.<n>It is the first large-scale, taxonomy-aligned audit of institutional web resources in Australia.
• Score: 7.412243627099252
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Technology-Facilitated Abuse (TFA) encompasses a broad and rapidly evolving set of behaviours in which digital systems are used to harass, monitor, threaten, or control individuals. Although prior research has documented many forms of TFA, there is no consolidated framework for understanding how abuse types, prevention measures, detection mechanisms, and support pathways relate across the abuse life cycle. This paper contributes a unified, literature-derived taxonomy of TFA grounded in a structured review of peer-reviewed studies, and the first large-scale, taxonomy-aligned audit of institutional web resources in Australia. We crawl 306 government, non-government, and service-provider domains, obtaining 52,605 pages, and classify using zero-shot topic models to map web content onto our taxonomy. An emotion and readability analyses reveal how institutions frame TFA and how accessible their guidance is to the public. Our findings show that institutional websites cover only a narrow subset of harms emphasised in the literature, with approximately 70% of all abuse labelled pages focused on harassment, comments abuse, or sexual abuse, while less than 1% address covert surveillance, economic abuse, or long-term controlling behaviours. Support pathways are similarly limited, with most resources centred on digital information hubs rather than counselling or community-based services. Readability analysis further shows that much of this content is written at late secondary or early tertiary reading levels, which may be inaccessible to a substantial portion of at-risk users. By highlighting strengths and gaps in Australia's support for TFA, our taxonomy and audit method provide a scalable basis for evaluating institutional communication, improving survivor resources, and guiding safer digital ecosystems. The taxonomy serves as a foundation for analyses in national contexts to foster TFA awareness.

Related papers

• Deep Academic Survey: Stateful Agentic Closed-Loop Paradigm for Academic Survey Automation [60.151050016855834]
  DAS is a stateful agentic framework for generating publication-oriented academic surveys.<n>Its key idea is to separate reusable paper analysis from topic-specific manuscript construction.<n>DAS achieves the highest average in all four dimensions, with an overall score of 4.34 compared with 4.03 for the strongest competitor.
  arXiv  Detail & Related papers  (2026-08-18T17:29:00Z)
• Seeking Help in the Digital Age: A Cross-Platform Analysis of Online Support Systems for Technology-Facilitated Abuse Victims [3.4605921795640477]
  Technology-facilitated abuse (TFA) has become a pervasive form of interpersonal harm.<n>We present a large-scale evaluation of online support for TFA victims across three channels: web search, peer-support forums, and conversational AI systems.
  arXiv  Detail & Related papers  (2026-07-23T17:35:20Z)
• Big AI's Regulatory Capture: Mapping Industry Interference and Government Complicity [7.894837175061716]
  It is critical that we comprehend the extent and depth of pervasive and multifaceted capture of AI regulation by corporate actors.<n>We first develop a taxonomy of mechanisms enabling capture to provide a comprehensive understanding of the problem.<n>Our analysis identifies 249 instances of capture mechanisms, often co-occurring with narratives that rationalise such capture.
  arXiv  Detail & Related papers  (2026-05-07T18:07:57Z)
• When AI reviews science: Can we trust the referee? [73.47745294608072]
  We map attacks across the review lifecycle -- training and data retrieval, desk review, deep review, rebuttal, and system-level.<n>We use two advanced LLM-based referees to isolate the causal effects of prestige framing, assertion strength, rebuttal sycophancy, and contextual poisoning on review scores.
  arXiv  Detail & Related papers  (2026-04-26T08:03:32Z)
• Citation-Enforced RAG for Fiscal Document Intelligence: Cited, Explainable Knowledge Retrieval in Tax Compliance [0.0]
  This paper presents a citation-enforced RAG framework for fiscal document intelligence that prioritises explainability and auditability.<n> Evaluation on real IRS and state tax documents demonstrates improved citation fidelity, reduced provenance, and analyst-usable explanations.
  arXiv  Detail & Related papers  (2026-03-11T20:01:35Z)
• Stop DDoS Attacking the Research Community with AI-Generated Survey Papers [27.03595915182805]
  We argue that we must stop uploading massive amounts of AI-generated survey papers to the research community.<n>We call for restoring expert oversight and transparency in AI usage.<n>We show that safeguarding the integrity of surveys is no longer optional but imperative to the research community.
  arXiv  Detail & Related papers  (2025-10-09T05:55:13Z)
• SoK: Large Language Model Copyright Auditing via Fingerprinting [69.14570598973195]
  We introduce a unified framework and formal taxonomy that categorizes existing methods into white-box and black-box approaches.<n>We propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios.
  arXiv  Detail & Related papers  (2025-08-27T12:56:57Z)
• A Unifying Bias-aware Multidisciplinary Framework for Investigating Socio-Technical Issues [6.307051347179095]
  This paper aims to bring together the disciplines of social science (SS) and computer science (CS)<n>Various analysis approaches from social science and machine learning (ML) were applied in a structured sequence to arrive at an original methodology of identifying and quantifying objects of inquiry.<n>A core feature of this framework is that it highlights where bias occurs and suggests possible steps to mitigate it.<n>To test our framework, we utilised it in the multidisciplinary investigation of the online harms encountered by minoritised ethnic (ME) communities when accessing and using digitalised social housing services in the UK.
  arXiv  Detail & Related papers  (2025-05-06T14:52:04Z)
• Understanding and Mitigating Risks of Generative AI in Financial Services [22.673239064487667]
  We aim to highlight AI content safety considerations specific to the financial services domain and outline an associated AI content risk taxonomy.<n>We evaluate how existing open-source technical guardrail solutions cover this taxonomy by assessing them on data collected via red-teaming activities.
  arXiv  Detail & Related papers  (2025-04-25T16:55:51Z)
• Illusions of Relevance: Using Content Injection Attacks to Deceive Retrievers, Rerankers, and LLM Judges [52.96987928118327]
  We find that embedding models for retrieval, rerankers, and large language model (LLM) relevance judges are vulnerable to content injection attacks.<n>We identify two primary threats: (1) inserting unrelated or harmful content within passages that still appear deceptively "relevant", and (2) inserting entire queries or key query terms into passages to boost their perceived relevance.<n>Our study systematically examines the factors that influence an attack's success, such as the placement of injected content and the balance between relevant and non-relevant material.
  arXiv  Detail & Related papers  (2025-01-30T18:02:15Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)
• Technological Factors Influencing Videoconferencing and Zoom Fatigue [60.34717956708476]
  The paper presents a conceptual, multidimensional approach to understand the technological factors that are assumed to or even have been proven to contribute to Zoom Fatigue (ZF) or more generally Videoconferencing Fatigue (VCF) The paper is motivated by the fact that some of the media outlets initially starting the debate on what Zoom fatigue is and how it can be avoided, as well as some of the scientific papers addressing the topic, contain assumptions that are rather hypothetical and insufficiently underpinned by scientific evidence.
  arXiv  Detail & Related papers  (2022-02-03T18:02:59Z)
• The User behind the Abuse: A Position on Ethics and Explainability [25.791014642037585]
  We discuss the role that modeling of users and online communities plays in abuse detection. We then explore the ethical challenges of incorporating user and community information. We propose properties that an explainable method should aim to exhibit.
  arXiv  Detail & Related papers  (2021-03-31T16:20:37Z)
• Towards Ethics by Design in Online Abusive Content Detection [7.163723138100273]
  The research effort has spread out across several closely related sub-areas, such as detection of hate speech, toxicity, cyberbullying, etc. We bring ethical issues to forefront and propose a unified framework as a two-step process. The novel framework is guided by the Ethics by Design principle and is a step towards building more accurate and trusted models.
  arXiv  Detail & Related papers  (2020-10-28T13:10:24Z)
• Predicting Themes within Complex Unstructured Texts: A Case Study on Safeguarding Reports [66.39150945184683]
  We focus on the problem of automatically identifying the main themes in a safeguarding report using supervised classification approaches. Our results show the potential of deep learning models to simulate subject-expert behaviour even for complex tasks with limited labelled data.
  arXiv  Detail & Related papers  (2020-10-27T19:48:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.