Search arXiv⌕ Search

arXiv · 2108.09939

Transcending Old Boundaries: Digital Afterlife in the Age of COVID-19

Abstract

The primary objective of our exploratory research is to contribute to the ongoing conversation on Digital Afterlife from the lenses of Global South during the COVID-19 period. Digital Afterlife is fast becoming a challenge for our increasingly connected society. Moreover, the situation got worse with the COVID-19 pandemic. The on-going research is to address the disparity in the Global South, specifically in countries like Indonesia, India and The Philippines compared to the Global North for Digital Afterlife services such as policies and digital mourning services. By addressing the research question, 'What services and policy frameworks are available for Digital Afterlife in the Global South during COVID-19?', we aim to find the multitude of ways people in the Global South are managing their digital footprints. Our preliminary findings show that some considerable research and death related digital services and innovation have taken place during the pandemic. However, overwhelming majority of these works are western-centric and mainly dealing with post-mortem personal asset management. Cultural nuances, socio-economic perspectives, religion, political climate, regional infrastructures are mostly sidelined. We found significant disparity in Digital Afterlife product and service designs, which got worse during the global pandemic. Our goal is to collect further in-depth data within the three big ICT powerhouses of global south (Indonesia, India and The Philippines), identify the challenges as well as the innovations around Digital Afterlife.We envision proposing a set of recommendations, based on our findings, for developing a more inclusive and equitable digital space in this pandemic-stricken world.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mashiat Mostafa, Faheem Hussain. 2021-08-23. Transcending Old Boundaries: Digital Afterlife in the Age of COVID-19. https://arxiv.org/abs/2108.09939

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Political Persuasion and Endorsement in Large Language Models

Large Language Models (LLMs) are increasingly employed as proxies for human behavior in computational social science. However, their tendency to internalize biases from training data raises concerns about their reliability in politically sensitive domains, specifically regarding their endorsement of persuasive language. In this work, we examine whether LLMs endorse persuasion-infused messages and whether partisan persona prompting modulates such endorsement. We evaluate six LLMs from different geographic regions on content annotated with persuasion techniques drawn from real-world media sources, measuring the likelihood of endorsement using a five-point Likert scale. The models are prompted as either a neutral social media user or as a user with left- or right-leaning political views. Results show that, without political conditioning, LLMs generally do not endorse messages containing persuasion techniques, although model-level differences emerge, and that partisan persona prompting increases polarization of endorsement, particularly for persuasion-infused content. Endorsement further varies across persuasion techniques and topics. These findings reveal how political conditioning influences LLMs' endorsement behavior toward persuasion-infused content, raising concerns about their deployment as agents in politically sensitive environments and complicating their use as reliable simulators of human political cognition.

cs.CY↗

When Technically Plausible Advice Is Unsafe: A Cross-Ecosystem Measurement of Online Support for Technology-Facilitated Abuse

Technology-facilitated abuse (TFA) creates an adversarial setting where sound cybersecurity advice can be unsafe: changing credentials or resetting devices may alert an abuser, destroy evidence, or increase escalation risk. Victims seek guidance from search engines, peer forums, and conversational AI, often evaluated for relevance and correctness rather than contextual safety. We measure whether these sources meet victims' needs. From a decade of r/Stalking narratives, we construct 2,797 victim-derived queries spanning 11 misuse categories. We analyze 27,162 Google webpages, 2,476 Reddit query--thread responses, and 250 responses from three general-purpose LLMs and two survivor-support chatbots. Our framework measures technical quality and damaging guidance, plus secondary-link integrity on webpages, toxicity on Reddit, and trauma-informed support in conversational systems. We find failures & risks that relevance, accuracy, or actionability alone do not capture. Web Search and conversational systems frequently return relevant information; Reddit responses are less consistently relevant and actionable. In our evaluated accuracy sample, 17.3% of webpages, 13.3% of Reddit threads, and 19.6% of conversational AI responses contained damaging guidance. Further, 65.5% of victim queries led to a webpage with a secondary URL flagged by multiple VirusTotal engines, over 20% received a toxic Reddit comment, and every conversational system produced guidance that overlooked escalation risk. Specialization did not guarantee better support: HopeChat underperformed general-purpose LLMs on several dimensions, while Ruth remained limited in trauma-informed support. These findings expose a gap between technical quality and contextual safety. Safe TFA assistance requires risk-aware recommendations, trustworthy sources, uncertainty communication, and human support, beyond technically plausible answers.

cs.CY↗

Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks

Health systems are rapidly deploying generative-AI assistants that answer patient questions from institution-authored education materials, on the premise that grounding in local content yields consistent guidance. Do the underlying documents themselves agree? We use a structured-output large-language-model judge to audit 1{,}772{,}261 pairwise comparisons across 102 patient-education handbooks from 23 US solid-organ transplant centers, paired with 1{,}115 patient-derived questions (TransplantQA). Four findings bear directly on deployment: (1) same-center cross-organ agreement exceeds cross-center same-organ agreement by $0.024$ in the primary analysis (Holm-adjusted $p=0.011$), with sensitivity to document selection; (2) information gaps concern topics relevant to underrepresented subgroups, with reproductive health a \emph{double jeopardy}: 82\% absence and 86\% judge-rated high significance among divergent/contradictory pairs; (3) judge-derived themes form 991 clusters, with immunosuppression and pregnancy timing among the highest judge-rated priorities; (4) question and observed-coverage features predict high-divergence questions retrospectively (AUC $0.77$). We discuss implications for deploying patient-facing generative AI in transplant care.

cs.CY↗