Computers and Society
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Showing new listings for Friday, 28 March 2025
- [1] arXiv:2503.20833 [pdf, other]
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Title: The Oxford Insights Government AI Readiness Index (GARI): An Analysis of its Data and Overcoming Obstacles, with a Case Study of IraqComments: 18 pages, 5 figuresSubjects: Computers and Society (cs.CY)
This research examines the "Government AI Readines Index" (GARI) issued by Oxford, analyzing data on governmental preparedness for adopting artificial intelligence acros different countrie. It highlights the evaluation criteria used to assess readiness, including technological infrastructure, human resources, supportive policies, and the level of innovation.
The study specifically focuses on Iraq, exploring the challenge the Iraqi government face in adopting and implementing AI technology. It discussed economic, social, and political barriers that hinder this transition and provides concrete recommendations to overcome these obstacle.
By analyzing Iraq case, the research aims to offer insight into improving collaboration between the public and private sectors to enhance the effective use of AI in governance and public administration. Additionally, the study emphasizes the importance of investing in education, training, and capacity building to develop a skilled workforce, enabling countries to harness AI potential and improve government service efficiency. - [2] arXiv:2503.20986 [pdf, html, other]
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Title: Musical Chairs: A new benchmark to evaluate AIComments: 16 pages, 3 figures, accepted at this https URLSubjects: Computers and Society (cs.CY); Theoretical Economics (econ.TH)
This paper presents a new contribution to the growing set of benchmarks used to prune potential AI designs. Much as one might evaluate a machine in terms of its performance at chess, this benchmark involves testing a machine in terms of its performance at a game called "Musical Chairs." At the time of writing, Claude, ChatGPT, and Qwen each failed this test, so the test could aid in their ongoing improvement. Furthermore, this paper sets a stage for future innovation in game theory and AI safety by providing an example of success with non-standard approaches to each: studying a game beyond the scope of previous game theoretic tools and mitigating a serious AI safety risk in a way that requires neither determination of values nor their enforcement.
- [3] arXiv:2503.20989 [pdf, html, other]
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Title: Inferring fine-grained migration patterns across the United StatesSubjects: Computers and Society (cs.CY)
Fine-grained migration data illuminate important demographic, environmental, and health phenomena. However, migration datasets within the United States remain lacking: publicly available Census data are neither spatially nor temporally granular, and proprietary data have higher resolution but demographic and other biases. To address these limitations, we develop a scalable iterative-proportional-fitting based method which reconciles high-resolution but biased proprietary data with low-resolution but more reliable Census data. We apply this method to produce MIGRATE, a dataset of annual migration matrices from 2010 - 2019 which captures flows between 47.4 billion pairs of Census Block Groups -- about four thousand times more granular than publicly available data. These estimates are highly correlated with external ground-truth datasets, and improve accuracy and reduce bias relative to raw proprietary data. We publicly release MIGRATE estimates and provide a case study illustrating how they reveal granular patterns of migration in response to California wildfires.
- [4] arXiv:2503.21162 [pdf, html, other]
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Title: Network Density Analysis of Health Seeking Behavior in Metro Manila: A Retrospective Analysis on COVID-19 Google Trends DataComments: Pre-print conference submission to ICMHI 2025, which it has been accepted. This has 12 pages, and 2 figuresSubjects: Computers and Society (cs.CY); Information Retrieval (cs.IR)
This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five categories (English symptoms, Filipino symptoms, face wearing, quarantine, and new normal) were examined using both 15-day and 30-day rolling windows from March 2020 to March 2021. The methodology involved constructing network graphs using distance correlation coefficients at varying thresholds (0.4, 0.5, 0.6, and 0.8) and analyzing the time-series data of network density and clustering coefficients. Results revealed three key findings: (1) an inverse relationship between the threshold values and network metrics, indicating that higher thresholds provide more meaningful keyword relationships; (2) exceptionally high network connectivity during the initial pandemic months followed by gradual decline; and (3) distinct patterns in keyword relationships, transitioning from policy-focused searches to more symptom-specific queries as the pandemic temporally progressed. The 30-day window analysis showed more stable, but less search activities compared to the 15-day windows, suggesting stronger correlations in immediate search behaviors. These insights are helpful for health communication because it emphasizes the need of a strategic and conscientious information dissemination from the government or the private sector based on the networked search behavior (e.g. prioritizing to inform select symptoms rather than an overview of what the coronavirus is).
- [5] arXiv:2503.21497 [pdf, html, other]
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Title: Behavioral response to mobile phone evacuation alertsErick Elejalde, Timur Naushirvanov, Kyriaki Kalimeri, Elisa Omodei, Márton Karsai, Loreto Bravo, Leo FerresSubjects: Computers and Society (cs.CY); Social and Information Networks (cs.SI)
This study examines behavioral responses to mobile phone evacuation alerts during the February 2024 wildfires in Valparaíso, Chile. Using anonymized mobile network data from 580,000 devices, we analyze population movement following emergency SMS notifications. Results reveal three key patterns: (1) initial alerts trigger immediate evacuation responses with connectivity dropping by 80\% within 1.5 hours, while subsequent messages show diminishing effects; (2) substantial evacuation also occurs in non-warned areas, indicating potential transportation congestion; (3) socioeconomic disparities exist in evacuation timing, with high-income areas evacuating faster and showing less differentiation between warned and non-warned locations. Statistical modeling demonstrates socioeconomic variations in both evacuation decision rates and recovery patterns. These findings inform emergency communication strategies for climate-driven disasters, highlighting the need for targeted alerts, socioeconomically calibrated messaging, and staged evacuation procedures to enhance public safety during crises.
New submissions (showing 5 of 5 entries)
- [6] arXiv:2503.20793 (cross-list from cs.SI) [pdf, html, other]
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Title: Semantic Web -- A Forgotten Wave of Artificial Intelligence?Comments: 21 pages, 9 figuresSubjects: Social and Information Networks (cs.SI); Computers and Society (cs.CY)
The history of Artificial Intelligence is a narrative of waves - rising optimism followed by crashing disappointments. AI winters, such as the early 2000s, are often remembered as barren periods of innovation. This paper argues that such a perspective overlooks a crucial wave of AI that seems to be forgotten: the rise of the Semantic Web, which is based on knowledge representation, logic, and reasoning, and its interplay with intelligent Software Agents. Fast forward to today, and ChatGPT has reignited AI enthusiasm, built on deep learning and advanced neural models. However, before Large Language Models dominated the conversation, another ambitious vision emerged - one where AI-driven Software Agents autonomously served Web users based on a structured, machine-interpretable Web. The Semantic Web aimed to transform the World Wide Web into an ecosystem where AI could reason, understand, and act. Between 2000 and 2010, this vision sparked a significant research boom, only to fade into obscurity as AI's mainstream narrative shifted elsewhere. Today, as LLMs edge toward autonomous execution, we revisit this overlooked wave. By analyzing its academic impact through bibliometric data, we highlight the Semantic Web's role in AI history and its untapped potential for modern Software Agent development. Recognizing this forgotten chapter not only deepens our understanding of AI's cyclical evolution but also offers key insights for integrating emerging technologies.
- [7] arXiv:2503.20797 (cross-list from cs.CL) [pdf, html, other]
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Title: "Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration SelectionSubjects: Computation and Language (cs.CL); Computers and Society (cs.CY); Social and Information Networks (cs.SI)
The rapid growth of social media platforms has led to concerns about radicalization, filter bubbles, and content bias. Existing approaches to classifying ideology are limited in that they require extensive human effort, the labeling of large datasets, and are not able to adapt to evolving ideological contexts. This paper explores the potential of Large Language Models (LLMs) for classifying the political ideology of online content in the context of the two-party US political spectrum through in-context learning (ICL). Our extensive experiments involving demonstration selection in label-balanced fashion, conducted on three datasets comprising news articles and YouTube videos, reveal that our approach significantly outperforms zero-shot and traditional supervised methods. Additionally, we evaluate the influence of metadata (e.g., content source and descriptions) on ideological classification and discuss its implications. Finally, we show how providing the source for political and non-political content influences the LLM's classification.
- [8] arXiv:2503.20806 (cross-list from cs.CR) [pdf, html, other]
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Title: SCVI: Bridging Social and Cyber Dimensions for Comprehensive Vulnerability AssessmentShutonu Mitra, Tomas Neguyen, Qi Zhang, Hyungmin Kim, Hossein Salemi, Chen-Wei Chang, Fengxiu Zhang, Michin Hong, Chang-Tien Lu, Hemant Purohit, Jin-Hee ChoSubjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY)
The rise of cyber threats on social media platforms necessitates advanced metrics to assess and mitigate social cyber vulnerabilities. This paper presents the Social Cyber Vulnerability Index (SCVI), a novel framework integrating individual-level factors (e.g., awareness, behavioral traits, psychological attributes) and attack-level characteristics (e.g., frequency, consequence, sophistication) for comprehensive socio-cyber vulnerability assessment. SCVI is validated using survey data (iPoll) and textual data (Reddit scam reports), demonstrating adaptability across modalities while revealing demographic disparities and regional vulnerabilities. Comparative analyses with the Common Vulnerability Scoring System (CVSS) and the Social Vulnerability Index (SVI) show the superior ability of SCVI to capture nuanced socio-technical risks. Monte Carlo-based weight variability analysis confirms SCVI is robust and highlights its utility in identifying high-risk groups. By addressing gaps in traditional metrics, SCVI offers actionable insights for policymakers and practitioners, advancing inclusive strategies to mitigate emerging threats such as AI-powered phishing and deepfake scams.
- [9] arXiv:2503.20821 (cross-list from cs.CR) [pdf, html, other]
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Title: "Hello, is this Anna?": A First Look at Pig-Butchering ScamsSubjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY)
Pig-butchering scams, or Sha Zhu Pan, have emerged as a complex form of cyber-enabled financial fraud that combines elements of romance, investment fraud, and advanced social engineering tactics to systematically exploit victims. In this paper, we present the first qualitative analysis of pig-butchering scams, informed by in-depth semi-structured interviews with N=26 victims. We capture nuanced, first-hand accounts from victims across multiple regions, providing insight into the lifecycle of pig-butchering scams and the complex emotional and financial manipulation involved. We systematically analyze each phase of the scam, revealing that perpetrators employ tactics such as staged trust-building, fraudulent financial platforms, fabricated investment returns, and repeated high-pressure tactics, all designed to exploit victims' trust and financial resources over extended periods. Our findings reveal an organized scam lifecycle characterized by emotional manipulation, staged financial exploitation, and persistent re-engagement efforts that amplify victim losses. We also find complex psychological and financial impacts on victims, including heightened vulnerability to secondary scams. Finally, we propose actionable intervention points for social media and financial platforms to curb the prevalence of these scams and highlight the need for non-stigmatizing terminology to encourage victims to report and seek assistance.
- [10] arXiv:2503.20847 (cross-list from cs.DB) [pdf, html, other]
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Title: The Data Sharing Paradox of Synthetic Data in HealthcareJim Achterberg, Bram van Dijk, Saif ul Islam, Hafiz Muhammad Waseem, Parisis Gallos, Gregory Epiphaniou, Carsten Maple, Marcel Haas, Marco SpruitComments: Accepted for publication at Medical Informatics Europe 2025 conference, GlasgowSubjects: Databases (cs.DB); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
Synthetic data offers a promising solution to privacy concerns in healthcare by generating useful datasets in a privacy-aware manner. However, although synthetic data is typically developed with the intention of sharing said data, ambiguous reidentification risk assessments often prevent synthetic data from seeing the light of day. One of the main causes is that privacy metrics for synthetic data, which inform on reidentification risks, are not well-aligned with practical requirements and regulations regarding data sharing in healthcare. This article discusses the paradoxical situation where synthetic data is designed for data sharing but is often still restricted. We also discuss how the field should move forward to mitigate this issue.
- [11] arXiv:2503.20848 (cross-list from cs.GT) [pdf, html, other]
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Title: The Backfiring Effect of Weak AI Safety RegulationComments: 28 pages, 8 figuresSubjects: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Theoretical Economics (econ.TH)
Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches to AI safety. We present a strategic model that explores the interactions between the regulator, the general-purpose AI technology creators, and domain specialists--those who adapt the AI for specific applications. Our analysis examines how different regulatory measures, targeting different parts of the development chain, affect the outcome of the development process. In particular, we assume AI technology is described by two key attributes: safety and performance. The regulator first sets a minimum safety standard that applies to one or both players, with strict penalties for non-compliance. The general-purpose creator then develops the technology, establishing its initial safety and performance levels. Next, domain specialists refine the AI for their specific use cases, and the resulting revenue is distributed between the specialist and generalist through an ex-ante bargaining process. Our analysis of this game reveals two key insights: First, weak safety regulation imposed only on the domain specialists can backfire. While it might seem logical to regulate use cases (as opposed to the general-purpose technology), our analysis shows that weak regulations targeting domain specialists alone can unintentionally reduce safety. This effect persists across a wide range of settings. Second, in sharp contrast to the previous finding, we observe that stronger, well-placed regulation can in fact benefit all players subjected to it. When regulators impose appropriate safety standards on both AI creators and domain specialists, the regulation functions as a commitment mechanism, leading to safety and performance gains, surpassing what is achieved under no regulation or regulating one player only.
- [12] arXiv:2503.20960 (cross-list from cs.CL) [pdf, html, other]
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Title: Multi-Modal Framing Analysis of NewsSubjects: Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its reception. So far, such studies have been narrow, in that they use a fixed set of pre-defined frames and focus only on the text, ignoring the visual contexts in which those texts appear. Especially for framing in the news, this leaves out valuable information about editorial choices, which include not just the written article but also accompanying photographs. To overcome such limitations, we present a method for conducting multi-modal, multi-label framing analysis at scale using large (vision-)language models. Grounding our work in framing theory, we extract latent meaning embedded in images used to convey a certain point and contrast that to the text by comparing the respective frames used. We also identify highly partisan framing of topics with issue-specific frame analysis found in prior qualitative work. We demonstrate a method for doing scalable integrative framing analysis of both text and image in news, providing a more complete picture for understanding media bias.
- [13] arXiv:2503.21228 (cross-list from q-bio.PE) [pdf, html, other]
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Title: Value of risk-contact data from digital contact monitoring apps in infectious disease modelingMartijn H. H. Schoot Uiterkamp, Willian J. van Dijk, Hans Heesterbeek, Remco van der Hofstad, Jessica C. Kiefte-de Jong, Nelly LitvakComments: 15 pages, 5 figuresSubjects: Populations and Evolution (q-bio.PE); Computers and Society (cs.CY); Physics and Society (physics.soc-ph)
In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is, unlike that of traditionally collected data via, e.g., surveys during non-epidemic times. The core idea behind our method is to express the number of infectious individuals as a function of the proportion of contacts that were with infected individuals and use this number as a starting point to initialize the remaining compartments of the model. As an important consequence, using our method, we can estimate key indicators such as the effective reproduction number using only two types of daily aggregated contact information, namely the average number of contacts and the average number of those contacts that were with an infected individual. We apply our method to the recent COVID-19 epidemic in the Netherlands, using self-reported data from the health surveillance app COVID RADAR and proximity-based data from the contact tracing app CoronaMelder. For both data sources, our corresponding estimates of the effective reproduction number agree both in time and magnitude with estimates based on other more detailed data sources such as daily numbers of cases and hospitalizations. This suggests that the use of DCM data in transmission models, regardless of the precise data type and for example via our method, offers a promising alternative for estimating the state of an epidemic, especially when more detailed data are not available.
- [14] arXiv:2503.21679 (cross-list from cs.CL) [pdf, html, other]
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Title: JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human Self-Destructive Behavior Content in Jirai CommunityComments: 20 pages, 1 figuresSubjects: Computation and Language (cs.CL); Computers and Society (cs.CY)
This paper introduces JiraiBench, the first bilingual benchmark for evaluating large language models' effectiveness in detecting self-destructive content across Chinese and Japanese social media communities. Focusing on the transnational "Jirai" (landmine) online subculture that encompasses multiple forms of self-destructive behaviors including drug overdose, eating disorders, and self-harm, we present a comprehensive evaluation framework incorporating both linguistic and cultural dimensions. Our dataset comprises 10,419 Chinese posts and 5,000 Japanese posts with multidimensional annotation along three behavioral categories, achieving substantial inter-annotator agreement. Experimental evaluations across four state-of-the-art models reveal significant performance variations based on instructional language, with Japanese prompts unexpectedly outperforming Chinese prompts when processing Chinese content. This emergent cross-cultural transfer suggests that cultural proximity can sometimes outweigh linguistic similarity in detection tasks. Cross-lingual transfer experiments with fine-tuned models further demonstrate the potential for knowledge transfer between these language systems without explicit target language training. These findings highlight the need for culturally-informed approaches to multilingual content moderation and provide empirical evidence for the importance of cultural context in developing more effective detection systems for vulnerable online communities.
Cross submissions (showing 9 of 9 entries)
- [15] arXiv:2008.08025 (replaced) [pdf, html, other]
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Title: How to organize an in-person, online or hybrid hackathon -- A revised planning kitComments: 37 pages, 0 figuresSubjects: Computers and Society (cs.CY); Software Engineering (cs.SE)
Hackathons and similar time-bounded events are a global phenomenon. Their proliferation in various domains and their usefulness for a variety of goals has led to the emergence of different formats. While there are a multitude of guidelines available on how to prepare and run a hackathon, most of them focus on a particular format that was created for a specific purpose within a domain for a certain type of participant. This makes it difficult, in particular, for novice organizers to decide how to run an event that fits their needs. To address this gap we developed the original version of this planning kit in 2020 which focused on in-person events that were the dominant form of hackathons then. That planning kit was organized around 12 key decisions that organizers need to take when preparing for, running, and following up on a hackathon. Fast forward to 2025, after going through a global pandemic that forced all events to move online, we now see different forms of events - in-person, online, and hybrid - taking place across the globe, and while they can be all valuable, they have different affordances and require different considerations when planning. To account for these differences, we decided to update the original planning kit by adding a section that discusses the affordances and requirements of in-person, online, and hybrid events to each of the 12 decisions. In addition, we modified the original example timelines to include different forms and types of events. We also updated the planning kit in general based on insights we gained through continuing to organize and study hackathons. The main planning kit is available online while this report is meant to be a downloadable and citable resource.
- [16] arXiv:2408.16863 (replaced) [pdf, html, other]
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Title: Data-Driven Law Firm Rankings to Reduce Information Asymmetry in Legal DisputesSubjects: Computers and Society (cs.CY)
Selecting capable counsel can shape the outcome of litigation, yet evaluating law firm performance remains challenging. Widely used rankings prioritize prestige, size, and revenue rather than empirical litigation outcomes, offering little practical guidance. To address this gap, we build on the Bradley-Terry model and introduce a new ranking framework that treats each lawsuit as a competitive game between plaintiff and defendant law firms. Leveraging a newly constructed dataset of 60,540 U.S. civil lawsuits involving 54,541 law firms, our findings show that existing reputation-based rankings correlate poorly with actual litigation success, whereas our outcome-based ranking substantially improves predictive accuracy. These findings establish a foundation for more transparent, data-driven assessments of legal performance.
- [17] arXiv:2501.18038 (replaced) [pdf, other]
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Title: A Case Study in Acceleration AI Ethics: The TELUS GenAI Conversational AgentSubjects: Computers and Society (cs.CY)
Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, ethics is embedded. Subsequently, the paper illustrates the acceleration framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world ambiguities, the Telus experience indicates that acceleration AI ethics is a way of maximizing social responsibility through innovation, as opposed to sacrificing social responsibility for innovation, or sacrificing innovation for social responsibility.
- [18] arXiv:2503.05704 (replaced) [pdf, html, other]
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Title: Evaluating Prediction-based Interventions with Human Decision Makers In MindComments: To be presented at AISTATS 2025Subjects: Computers and Society (cs.CY)
Automated decision systems (ADS) are broadly deployed to inform and support human decision-making across a wide range of consequential settings. However, various context-specific details complicate the goal of establishing meaningful experimental evaluations for prediction-based interventions. Notably, current experiment designs rely on simplifying assumptions about human decision making in order to derive causal estimates. In reality, specific experimental design decisions may induce cognitive biases in human decision makers, which could then significantly alter the observed effect sizes of the prediction intervention. In this paper, we formalize and investigate various models of human decision-making in the presence of a predictive model aid. We show that each of these behavioural models produces dependencies across decision subjects and results in the violation of existing assumptions, with consequences for treatment effect estimation. This work aims to further advance the scientific validity of intervention-based evaluation schemes for the assessment of ADS deployments.