In response to escalating global and national commitments to net-zero carbon targets, there is an increasing imperative to enhance awareness of carbon dioxide (CO2) concentrations at the community level. This study investigates spatial patterns of CO2 emissions across Wrexham County Borough, which comprises 34 administrative communities in northeastern Wales. By integrating satellite-derived CO2 concentration datasets with official boundary data using Python-based geospatial libraries and online geographic information systems, we generated interactive spatial visualisations to identify persistent emission hotspots. The analysis reveals consistently elevated CO2 levels in the northeastern communities, particularly Rossett and Holt. These visual outputs offer transparent, reproducible, and accessible insights to support evidence-based environmental planning, public engagement, and local policy development.
Gender equality is a global concern. This study examines long-term changes in Javanese naming practices, focusing on gendered name length as a culturally grounded proxy for gendered dynamics in the Javanese people, the largest ethnic group in Indonesia and Southeast Asia. In Javanese culture, names are often regarded as prayers (Asma Kinarya Japa) and symbolic responsibilities (Kabotan Jeneng), where longer names may represent a greater number, specificity, or diversity of parental aspirations. Because the Javanese system lacks hereditary surnames as lineage markers, the length of personal names is especially a salient indicator of parental aspirations. Using a computational approach on a large-scale dataset of nearly 3 million voter records (N = 2 906 978), this study demonstrates that Javanese males historically received longer names than Javanese females. Over time, names lengthened for both genders, and the gender gap narrowed unevenly: first in urban areas, then in suburban areas, and finally rural areas. These patterns suggest that naming practices capture shifting parental orientations that, among other factors, may reflect changing gender norms within broader processes of social transformation.
The global coffee supply chain continues to face financial exclusion, value asymmetry, and structural inefficiencies that disadvantage smallholder farmers and cooperatives. Blockchain-enabled Coffee Supply Chain Finance (CSCF) has been promoted as a mechanism to automate transactions, enhance transparency, and support more equitable value distribution. This study conducts a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, analyzing 60 peer-reviewed publications during 2020–2024 to identify how blockchain is being adopted in CSCF. The findings show that blockchain especially when integrated with Internet of Things (IoT) and Artificial Intelligence (AI) improves traceability, lowers transaction frictions, and expands access to finance through smart contracts, tokenized assets, and blockchain-based crowdfunding models. These mechanisms reduce intermediary dependence and strengthen risk monitoring across supply-chain actors. However, adoption remains constrained by high implementation costs, interoperability challenges, regulatory uncertainty, and limited digital readiness among farmers and cooperatives. Unlike previous blockchain reviews focused mainly on traceability, this study provides the first structured synthesis linking blockchain functions to financing models, adoption determinants, and policy requirements in the coffee sector. The review offers strategic implications for cooperatives, policymakers, and financial institutions seeking to develop scalable and inclusive CSCF ecosystems.
Mobile fitness apps, popularized in the pandemic by rising health concerns, ease of use and flexibility, use wearable sensors to visualize user engagement for physical health, yet focus on generic rather than personalized health recommendations. To overcome this challenge, this paper proposes recommendation system centred around personalized physical activity suggestions, aims to enhance user motivation and encourage adherence to workout routines utilizing Generative Artificial Intelligence (GenAI) deep collaborative filtering. The solution offered here treats the recommendation problem as a sparse value prediction problem where user's missing future workout duration is predicted using GenAI models that are able to create multiple workout combinations based on the user profile. In order to improve the accuracy of the prediction, latent representations created from explicit user data with domain-specific side features, are processed using generative transformers to produce highly tailored workout plans. An ablation study is conducted to measure how these components aid in personalisation, particularly novel workout combinations not included in the training data at all are, which the system produces, and are claimed in the training data are provided. This research aims to create more intelligent fitness recommendation systems using GenAI and accurately recommend activities to sustain physical activity while improving health issues, engaging users.
The primary objective of this study is to analyze the impact of promotional broadcasting campaigns on the view-count of YouTube videos, which is a crucial factor for revenue generation among YouTubers. To achieve this objective, various mathematical models are proposed and rigorously tested to understand the effects of these promotional strategies on video view-counts. Additionally, Distance-Based Analysis (DBA) is employed to rank the effectiveness of the proposed models. This dual approach ensures a robust assessment of how different broadcasting strategies influence view-counts. The analysis reveals that different broadcasting strategies yield varying levels of effectiveness, with some approaches leading to a more substantial increase in view-count than others. This study addresses a gap in the existing literature by focusing specifically on the broadcasting phenomenon, which has been scarcely explored. The findings provide valuable insights for content creators and marketers, with Model IV outperforming others across several datasets, demonstrating its robustness in capturing promotional impact.
Equal pay is an essential component of gender equality, one of the Sustainable Development Goals of the United Nations. Using resume data of over ten million Chinese online job seekers in 2015, we study the current gender pay gap in China. The results show that on average women only earned 71.57% of what men earned in China. The gender pay gap exists across all age groups and educational levels. Contrary to the commonly held view that developments in education, economy, and a more open culture would reduce the gender pay gap, the fusion analysis of resume data and socio-economic data presents that they have not helped reach the gender pay equality in China. China seems to be stuck in a place where traditional methods cannot make further progress. Our analysis further shows that 81.47% of the variance in the gender pay gap can be potentially attributed to discrimination. In particular, compared with the unmarried, both the gender pay gap itself and proportion potentially attributed to discrimination of the married are larger, indicating that married women suffer greater inequality and more discrimination than unmarried ones. Taken together, we suggest that more research attention should be paid to the effect of discrimination in understanding gender pay gap based on the family constraint theory. We also suggest the Chinese government to increase investment in family-supportive policies and grants in addition to female education.
The exponential growth of social media has heightened concerns about digital addiction and its mental health consequences, particularly among younger populations. Existing digital health tools, including conversational agents and large language models, offer real-time support but often neglect the predictive value of structured behavioural data. This study introduces a machine learning framework to assess digital addiction risk using 3200 anonymised self-reports comprising screen time, social media engagement, sleep duration, and mental health indicators. Across multiple models, categorical boosting (CatBoost) achieves the highest performance (precision = 85.4%, receiver operating characteristic-area under the curve (ROC-AUC) = 0.93), outperforming extreme gradient boosting (XGBoost) and graph neural networks (GNN). A linear regression model provides interpretable correlations between behavioural variables and addiction risk. Structural equation modelling (SEM) reveals that anxiety and depression mediate the relationship between digital behaviours and addiction risk, offering causal insights into these pathways. Feature importance analysis identified excessive screen time, frequent social media checking, and reduced sleep as the most influential predictors. To translate findings into practice, K-means clustering generated behavioural risk profiles, enabling personalised, data-driven recommendations. While clinical validation remains a next step, this framework demonstrates how predictive modelling and clustering can inform scalable, non-invasive digital health interventions. By integrating machine learning with causal modelling and personalised intervention design, this study advances computational approaches to digital addiction and contributes to the broader discourse on artificial intelligence applications in mental health and social computing.
Morally controversial content, such as offensive and hateful images over social media, is especially challenging to categorize, given widespread disagreement in how people interpret and evaluate this content. Numerous studies argue that a range of subjective biases, such as partisan differences in moral reasoning, lead people not only to diverge in their classifications of controversial content, but also to resist any attempts to change their classification judgments via social influence. Yet, recent large-scale analyses of classification patterns over social media suggest that separate populations, such as democrats and republicans, can reach surprising levels of agreement in the categorization of inflammatory content like fake news and hate speech, despite considerable differences in their moral reasoning and worldview. This poses a fundamental puzzle: how can populations of diverse individuals who disagree in the interpretation of controversial content nevertheless arrive at highly similar decisions for the classification and removal of such content? Here, we use an online platform to test the hypothesis that structural symmetries in information exchange networks can synchronize convergence on decisions regarding the classification and removal of controversial images across independent networks, leading them to independently reproduce consistent systems of classification. We find that isolated individuals diverge considerably in their classification of controversial content, whereas separate, structurally similar networks independently synchronize in their classifications and content removal decisions, reducing partisan biases across all networks. We also find that when participant experience is compared to subjects evaluating content individually in the control condition, participants within synchronizing networks reported having significantly more positive feelings about their task, and experience significantly less emotional stress when evaluating controversial content.
The problem of Point-Of-Interest (POI) recommendation, based on the user's historical check-in records, determines whether a user checks in at specific POI. However, the user-POI data have a long-tail distribution phenomenon. To mitigate the sparsity of check-in data, it is a good idea to exploit the sufficient attributes of POI and recommend POls in both geography wise and category wise. Generally, this problem can be treated as two specific tasks with feature combination, ignoring cross-task dependencies and feature disentanglement. To address the aforementioned problems, this paper proposes a novel joint framework named InteractPOI, enabling two-stage interaction bewteen geography-wise and category-wise POI recommendations. Specifically, this paper comprehensively considers the sequence effect and the neighbor effect both from geography wise and category wise. For the first-stage interaction, we design a disentangled graph embedding model to distinguish different influencing factors from geography wise and category wise. For the second-stage interaction, we integrate a gating mechanism for feature fusion with a complementary algorithm for interactive optimization. Extensive experiments on two datasets demonstrate the superiority of the proposed model.
The paper critically evaluates the global discourses on algorithmic fairness, reviews key Western literature on artificial intelligence (AI) fairness, identifies twelve documented cases of algorithmic discrimination in Western contexts, and extends them for their analytical relevance to non-Western socio-political environments. The study applies these frameworks in particular to the Indian context, and proposes that India's entrenched socio-cultural structures—caste, religion, language, regional identity, and minority status—tends to misalign with Western paradigms of fairness. The proposed study identifies identity-specific factors unique to India that are likely to contribute to algorithmic oppression if unaddressed. A critical policy analysis of major documents shaping India's AI and digital governance landscape reveals that these critical factors remain largely unacknowledged. Indian policy responses tend to replicate Western techno-legal models without engaging indigenous socio-structural realities. The paper concludes that ethical AI governance in India must transcend imported normative models and instead be rooted in context-sensitive approaches that accommodates the nation's distinct social fabric, in order to prevent algorithm induced structural discrimination and ensure inclusive algorithmic justice.
Intuitively, social network users with more fans or friends can have a higher information forwarding threshold, however, previous studies seldom consider the threshold heterogeneity. This paper proposes an information diffusion model in which forwarding threshold is related to user degree. The results indicate that small-world networks have certain advantages compared with scale-free networks, but the difference is not obvious. The probability of forwarding information is positively related to the final proportion of known nodes, but there is an inflection point in the influence of this probability on the proportion of hesitant nodes. The proportions of known and hesitant nodes increase with the average degree of the networks. Known nodes decrease slowly as network size increases, whereas hesitant nodes show the opposite trend. As the strength of users' preference for information decreases, the proportion of known nodes first increases and then decreases. The peak number of known nodes is gradually delayed as the mean degree increases, but the proportion of hesitant nodes always decreases. The model can enhance the precision of diffusion predictions, offering actionable insights for applications such as targeted rumor control and optimized marketing strategies.
Large Language Models (LLMs) are being increasingly used as synthetic agents in social science, in applications ranging from augmenting survey responses to powering multi-agent simulations. This paper outlines cautions that should be taken when interpreting LLM outputs and proposes a pragmatic reframing for the social sciences in which LLMs are used as high-capacity pattern matchers for quasi-predictive interpolation under explicit scope conditions and not as substitutes for probabilistic inference. Practical guardrails such as independent draws, preregistered human baselines, reliability-aware validation, and subgroup calibration, are introduced so that researchers may engage in useful prototyping and forecasting while avoiding category errors.
This essay examines the intricate relationship between large language models (LLMs) and privacy, investigating the ethical and practical issues stemming from cutting-edge artificial intelligence (AI) technologies. The research delves into the evolving understanding of privacy in the digital era, with a specific emphasis on the risks posed by anthropomorphic AI design. The analysis highlights critical privacy concerns: (1) Trust and accountability: The lack of true moral agency in AI systems complicates traditional notions of trust and responsibility; (2) Nissenbaum's Contextual Integrity Framework as a tool to explore privacy issues in general and with LLM; (3) Data collection challenges: LLMs collect extensive user data, often without explicit consent, potentially breaching contextual privacy norms; (4) Anthropomorphism risks: Human-like AI interfaces can foster over-trust, leading users to share sensitive information inappropriately. This article underscores that privacy is a complex, multidimensional concept profoundly shaped by technological, cultural, and social forces. As AI technologies continue to advance, safeguarding privacy will necessitate a nuanced approach that strikes a balance between individual rights, societal needs, and technological progress. We conclude with user-oriented guidelines and future research directions, offering a comprehensive framework for understanding and addressing the privacy implications of LLMs.
The aim of this study is to understand the judicial reasoning process of anti-discrimination jurisprudence by utilizing a hybrid computational model. The advancement of hybrid computational legal studies that combine “law-as-code” and “law-as-data” approaches have led to promising techniques for tackling complex legal reasoning tasks as multifactor judicial reasoning standards. Following this hybrid model, this study conducts a statistical and multilayer perceptron (MLP) analysis of the judicial reasoning process of the multifactor Arlington Heights discriminatory purpose test based on an original hand-coded dataset of discrimination cases. The results of the study show that “sequence of events” predominates the other factors for predicting the outcome of discrimination cases, with “statistical impact” and “historical background” showing particularly weak effect on the outcome. Results show that completely removing the factor of disparate statistical impact (Impact) actually improves confidence in predicting discrimination. The conclusion of the study suggests that courts fail to give either disparate statistical impact or historical record of discrimination any meaningful cumulative import in determining discriminatory purpose. I conclude by observing the benefits of hybrid computational legal studies on interrogating more normative values in the law like transparency, fairness, and procedural justice.
Floor area ratio (FAR), the ratio of a building's total floor area to its land area, is a critical urban planning metric. The FAR control, a government-regulated mechanism for land resource allocation, plays a crucial role in balancing private and public interests. However, rigid administrative controls often lead to inefficiencies, suggesting that market-oriented FAR trading could improve fairness and resource allocation. This study employs a role-playing simulation game to examine the feasibility of FAR trading as a policy tool. Participants, acting as real estate developers and FAR bank intermediaries, engaged in FAR transactions under symmetric information conditions. Results indicate that market-driven FAR trading can increase FAR utilization by 29%, reduce average housing prices by 3.8%, and optimize land value distribution. The findings highlight the potential of market mechanisms to complement traditional administrative controls in urban planning.
The rise of online social platforms has enhanced connectivity and access to information. Still, it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order. In this paper, we introduce a unified framework for defining and classifying malicious social bots along three dimensions: behavior, interaction, and operation. We then present a comprehensive review of social bot detection methods, tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks, with particular emphasis on recent advances in group-level detection. We also explore the emerging paradigm of Large Language Model (LLM) based bot detection. This paper reviews the current state of research, identifies key challenges, and outlines future directions. It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.
Consumer agency in the digital age is increasingly constrained by systemic barriers and algorithmic manipulation, raising concerns about the authenticity of consumption choices. Nowadays, financial decision-making is shaped by external pressures like obligatory consumption, algorithmic persuasion, and unstable work schedules that erode financial autonomy. Obligatory consumption (like subscriptions and hidden fees) is intensified by digital ecosystems. Algorithmic tactics like Buy Now Pay Later (BNPL) and personalized recommendations lead to impulsive purchases. Unstable work schedules also undermine financial planning. Therefore, it is important to study how these factors impact consumption agency. To do so, we examine formal models grounded in discounted consumption with constraints that bound consumer agency. Specifically, we construct analytical scenarios in which consumers face fixed obligatory payments, algorithm-influenced impulsive consumption, or unpredictable income due to temporal instability. Using this framework, we demonstrate that even rational, utility-maximizing agents can experience early financial ruin when agency is limited across structural, behavioral, or temporal dimensions and how diminished autonomy directly impacts long-term financial well-being. Our central argument is that consumer agency must be treated as a value (not a given) requiring active cultivation, especially in digital ecosystems. The connection between our formal modeling and the central argument allows us to indicate that limitations on agency (whether structural, behavioral, or temporal) can be rigorously linked to measurable risks like financial instability. This connection thus also provides a basis for normative claims regarding consumption as a value, by anchoring them in a formally grounded analysis of consumer behavior. As solutions, we stress that promoting true consumer agency demands systemic interventions, regulations, and consumer education to support value deliberation and informed autonomous choices. We formally demonstrate how these measures strengthen agency.
Social perception refers to how individuals interpret and understand the social world. It is a foundational area of theory and measurement within the social sciences, particularly in communication, political science, psychology, and sociology. Classical models include the Stereotype Content Model (SCM), Dual Perspective Model (DPM), and Semantic Differential (SD). Extensive research has been conducted on these models. However, their interrelationships are still difficult to define using conventional comparison methods, which often lack efficiency, validity, and scalability. To tackle this challenge, we employ a text-based computational approach to quantitatively represent each theoretical dimension of the models. Specifically, we map key content dimensions into a shared semantic space using word embeddings and automate the selection of over 500 contrasting word pairs based on semantic differential theory. The results suggest that social perception can be organized around two fundamental components: subjective evaluation (e.g., how good or likable someone is) and objective attributes (e.g., power or competence). Furthermore, we validate this computational approach with the widely used Rosenberg's 64 personality traits, demonstrating improvements in predictive performance over previous methods, with increases of 19%, 13%, and 4% for the SD, DPM, and SCM dimensions, respectively. By enabling scalable and interpretable comparisons across these models, our findings would facilitate both theoretical integration and practical applications.
Modeling and analysis of complex social networks is an important topic in social computing. Graph convolutional networks (GCNs) are widely used for learning social network embeddings and social network analysis. However, real-world complex social networks, such as Facebook and Math, exhibit significant global structural and dynamic characteristics that are not adequately captured by conventional GCN models. To address the above issues, this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies (GSTGCN). Specifically, we innovatively design a graph coarsening strategy based on the importance of social membership to construct a dynamic diffusion process of graphs. This dynamic diffusion process can be viewed as using higher-order subgraph embeddings to guide the generation of lower-order subgraph embeddings, and we model this process using gate recurrent unit (GRU) to extract comprehensive global structural features of the graph and the evolutionary processes embedded among subgraphs. Furthermore, we design a new evolutionary strategy that incorporates a temporal self-attention mechanism to enhance the extraction of global temporal dependencies of dynamic networks by GRU. GSTGCN outperforms current state-of-the-art network embedding methods in important social networks tasks such as link prediction and financial fraud identification.
There exist many panel data decision problems in real life, and they take on obvious structural similarities and lag effects among decision objects or indicators, which are difficult to solve effectively based on traditional panel data analysis methods. To deal with these problems, considering the structural characteristics of panel data and lag effect, from multiple structural dimensions such as scale volume, development trend, and volatility, we exploit grey incidence analysis and panel data to establish an indicator-type grey structural incidence analysis model, and utilize it to analyze and identify factors influencing technological innovation of industrial enterprises. The results show that the proposed method fully considers the structural characteristics of panel data and lag effect, and it can deal with panel data decision problems and provide a new methodological support for the grey incidence analysis.
