Experiencer, Helper, or Observer: Online Fraud Intervention for Older Adults Through Role-based Simulation
- URL: http://arxiv.org/abs/2601.12324v1
- Date: Sun, 18 Jan 2026 09:15:51 GMT
- Title: Experiencer, Helper, or Observer: Online Fraud Intervention for Older Adults Through Role-based Simulation
- Authors: Yue Deng, Xiaowei Chen, Junxiang Liao, Bo Li, Yixin Zou,
- Abstract summary: ROLESafe is an anti-fraud educational intervention in which older adults learn through different learning roles.<n>In a study with 144 older adults in China, we found that the Experiencer and Helper roles significantly improved participants' ability to identify online fraud.
- Score: 14.8124073941176
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Online fraud is a critical global threat that disproportionately targets older adults. Prior anti-fraud education for older adults has largely relied on static, traditional instruction that limits engagement and real-world transfer, whereas role-based simulation offers realistic yet low-risk opportunities for practice. Moreover, most interventions situate learners as victims, overlooking that fraud encounters often involve multiple roles, such as bystanders who witness scams and helpers who support victims. To address this gap, we developed ROLESafe, an anti-fraud educational intervention in which older adults learn through different learning roles, including Experiencer (experiencing fraud), Helper (assisting a victim), and Observer (witnessing fraud). In a between-subjects study with 144 older adults in China, we found that the Experiencer and Helper roles significantly improved participants' ability to identify online fraud. These findings highlight the promise of role-based, multi-perspective simulations for enhancing fraud awareness among older adults and provide design implications for future anti-fraud education.
Related papers
- Self-Consolidation for Self-Evolving Agents [51.94826934403236]
Large language model (LLM) agents operate as static systems, lacking the ability to evolve through lifelong interaction.<n>We propose a novel self-evolving framework for LLM agents that introduces a complementary evolution mechanism.
arXiv Detail & Related papers (2026-02-02T11:16:07Z) - Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts [54.15982476754607]
Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks.<n>This study defines complicit facilitation as the provision of guidance or support that enables illicit user instructions.<n>Using real-world legal cases and established legal frameworks, we construct an evaluation benchmark spanning 269 illicit scenarios and 50 illicit intents.
arXiv Detail & Related papers (2025-11-25T16:01:31Z) - When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms [101.2197679948061]
We study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents.<n>We present MultiAgentFraudBench, a large-scale benchmark for simulating financial fraud scenarios.
arXiv Detail & Related papers (2025-11-09T16:30:44Z) - Intergenerational Support for Deepfake Scams Targeting Older Adults [1.3871135653459332]
Deepfake scams produce convincing audio and visual impersonations of trusted family members, often grandchildren, in real time.<n>These attacks fabricate urgent scenarios, such as legal or medical emergencies, to socially engineer older adults into transferring money.<n>This study explores older adults' perceptions of these emerging threats and their responses.<n>We identify opportunities to engage youth as active partners in enhancing resilience across generations.
arXiv Detail & Related papers (2025-08-15T16:37:59Z) - Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements [23.99500412996251]
Fraud-R1 comprises 8,564 fraud cases sourced from phishing scams, fake job postings, social media, and news, categorized into 5 major fraud types.<n>Unlike previous benchmarks, Fraud-R1 introduces a multi-round evaluation pipeline to assess LLMs' resistance to fraud at different stages.<n>Our evaluation reveals the significant challenges in defending against fraud and phishing inducement, especially in role-play settings and fake job postings.
arXiv Detail & Related papers (2025-02-18T14:47:02Z) - "It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams [21.992539308179126]
We propose a framework for modeling scam calls and introduce an LLM-based real-time detection approach.<n>We evaluate the method's performance and analyze key factors influencing its effectiveness.
arXiv Detail & Related papers (2025-02-06T10:57:05Z) - Proactive Fraud Defense: Machine Learning's Evolving Role in Protecting Against Online Fraud [0.0]
This paper explores the transformative role of machine learning in fraud detection and prevention.
It highlights the strengths of machine learning in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions.
It emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries.
arXiv Detail & Related papers (2024-10-26T21:34:28Z) - Older adults' safety and security online: A post-pandemic exploration of attitudes and behaviors [0.0]
The behaviors and attitudes of a group of older adults aged 60 years and older regarding different dimensions of online safety and cybersecurity are investigated.<n>Results show that older adults report a discernible degree of concern about the security of their personal information.<n>Support systems should include older adults in the development of protective measures and acknowledge their diversity.
arXiv Detail & Related papers (2024-03-14T09:22:16Z) - Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations [58.96953392466609]
We take an in-depth look at the causal awareness of modern representations of agent interactions.<n>We show that recent representations are already partially resilient to perturbations of non-causal agents.<n>We introduce a metric learning approach that regularizes latent representations with causal annotations.
arXiv Detail & Related papers (2023-12-07T18:57:03Z) - Fragments of the Past: Curating Peer Support with Perpetrators of
Domestic Violence [88.37416552778178]
We report on a ten-month study where we worked with six support workers and eighteen perpetrators in the design and deployment of Fragments of the Past.
We share how crafting digitally-augmented artefacts - 'fragments' - of experiences of desisting from violence can translate messages for motivation and rapport between peers.
These insights provide the basis for practical considerations for future network design with challenging populations.
arXiv Detail & Related papers (2021-07-09T22:57:43Z) - Adversarial Visual Robustness by Causal Intervention [56.766342028800445]
Adversarial training is the de facto most promising defense against adversarial examples.
Yet, its passive nature inevitably prevents it from being immune to unknown attackers.
We provide a causal viewpoint of adversarial vulnerability: the cause is the confounder ubiquitously existing in learning.
arXiv Detail & Related papers (2021-06-17T14:23:54Z) - PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via
Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning.
We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
arXiv Detail & Related papers (2021-06-09T14:10:50Z)
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.