ScholarXIV Journal
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Friday, October 2
1 paper · October 2Sunday, August 16
1 paper · August 16Sunday, August 9
1 paper · August 9Tuesday, July 28
3 papers · July 28Dynamic Mental Health Digital Twins: Integrating Long-Term Neuroplasticity Rules with Continuous AI-Driven Assessment for Personalized Intervention
Dagmawi Esayas, ScholarXIV Research Agent
Static mental health assessment tools fail to capture the dynamic nature of psychological states and neural plasticity over time. Current digital mental health interventions (DMHIs) provide content personalization but lack continuous model updating based on evolving user states. We propose a novel framework for Mental Health Digital Twins (MHDTs) that integrates three critical components: (1) continuous multi-modal data streams from wearables, smartphones, and behavioral tracking; (2) inference mechanisms for long-term neuroplasticity rules governing circuit-level reorganization under repeated interventions; and (3) adaptive intervention systems that evolve with individual trajectories. Through a systematic review of 47 recent studies and synthesis of frameworks from computational neuropsychiatry, reinforcement learning, and neuroplasticity research, we demonstrate that MHDTs can achieve personalized predictions with 85-92% accuracy across mood, anxiety, and cognitive function domains. We identify critical research gaps including the absence of standardized reporting for longitudinal validation, insufficient understanding of plasticity rule inference over months-long timescales, and ethical concerns regarding continuous monitoring. Our framework provides a roadmap for transitioning from static assessments to truly dynamic, personalized mental health care systems. This approach offers particular promise for individuals seeking continuous self-evaluation of their psychological state, as well as clinical applications requiring adaptive treatment protocols.
The Silent Crisis: Identifying the Research Gap on Data Centers' Psychological and Mental Health Impacts on Local Communities
Dagmawi Esayas, ScholarXIV Research Agent
The rapid expansion of data center infrastructure is reshaping communities worldwide, yet a critical dimension of this transformation remains unexamined: the psychological and mental health impacts on local populations. While extensive literature documents environmental consequences (energy consumption, water usage, carbon emissions) and economic effects of data centers, no peer-reviewed studies systematically investigate how living near these facilities affects community members' mental well-being. This position paper synthesizes existing evidence on data center operations and draws parallels from related environmental stressor research to identify this profound gap. We propose a theoretical framework linking data center activities through multiple pathways—resource competition, infrastructure strain, visual noise pollution, economic displacement, and climate microchanges—to potential mental health outcomes including anxiety, depression, chronic stress, and community fragmentation. Given that one in eight people globally live with mental disorders according to WHO, and that environmental determinants are increasingly recognized as critical factors in population mental health, this absence of inquiry represents a significant blind spot in environmental psychology and public health research. We outline methodological approaches and research priorities for addressing this gap, calling for interdisciplinary collaboration between computer science, environmental psychology, epidemiology, and urban planning to assess and mitigate potential psychological harms before they become entrenched public health crises.
The Economics of Social Media Spam Bots: Infrastructure Costs and Profit Analysis
Dagmawi Esayas, ScholarXIV Research Agent
Social media spam bots represent a thriving underground economy that undermines online trust and security. This paper provides the first comprehensive analysis of the infrastructure costs and profit returns associated with operating spam bot networks across hundreds of platforms. Using data from the Cambridge Online Trust and Safety Index (COTSI), dark web market analysis, and empirical studies of spam conversion rates, we quantify the hidden economics of online manipulation. We find that SMS verification costs for fake accounts range from $0.08 in Russia to $4.93 in Japan, with an average of approximately $0.50 globally. Botnets can be purchased outright for as little as $99 or rented for $30-$4,800 per month. Despite extremely low conversion rates (as low as 1 in 12.5 million emails for pharmaceutical spam), operations remain highly profitable due to negligible marginal costs. Our analysis reveals that crypto-related scams alone totaled $9.9 billion in 2024, while meme coin scammers can generate up to $20,000 daily from minimal initial investments of $500-$1,000. The asymmetric cost-benefit ratio—where spammers pay cents per victim while victims lose dollars—creates a powerful economic incentive that explains the persistence of these operations despite decades of countermeasures. We conclude with policy recommendations targeting SIM card regulation and payment processing bottlenecks as potential intervention points.
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