While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application programming interfaces (APIs). This paper conceptualizes LLMs as universal text processing machines and presents a comprehensive workflow for employing LLMs in three qualitative and quantitative content analysis tasks: (1) annotation (an umbrella term for qualitative coding, labeling and text classification), (2) summarization, and (3) information extraction. The workflow is explicitly human-centered. Researchers design, supervise, and validate each stage of the LLM process to ensure rigor and transparency. Our approach synthesizes insights from extensive methodological literature across multiple disciplines: political science, sociology, computer science, psychology, and management. We outline validation procedures and best practices to address key limitations of LLMs, such as their black-box nature, prompt sensitivity, and tendency to hallucinate. To facilitate practical implementation, we provide supplementary materials, including a prompt library and Python code in Jupyter Notebook format, accompanied by detailed usage instructions.
Marta Bottero, Caterina Caprioli, Marcus Foth
et al.
Increasing concerns about sustainable development and climate change have pushed public and private actors and organisations to intensify their efforts to embed these issues in their plans, projects, programmes, and strategies. Within this context, the article examines sustainability as it relates to the Olympic Games, providing an overview of the measures taken by the International Olympic Committee (IOC) and the attention that these events' organisers have paid to integrating it over time. A comparative analysis of two cases – the 2006 Winter Olympic Games in Turin, Italy, and the 2032 Summer Olympic Games in Brisbane, Australia – has been developed to identify specificities and opportunities in staging such a global event, examining diverse and interconnected aspects of sustainability that could help evaluate it in future games. The article highlights that interpretations of the meaning of sustainability change over time and vary across stakeholders and that the long-term impact assessment of the legacy of such events requires further research.
This work lays the foundations for a rigorous ontological characterization of love, addressing its philosophical complexity and scientific relevance, with particular emphasis on psychology and sociology, as well as highlighting ways in which such characterization enhances relevant AI based applications. The position defended here is that love is best understood as a concatenation of passive sensations (e.g., emotional arousal) and active evaluative judgments (e.g., perceiving the beloved as valuable), in the interest of balancing the involuntary aspects of love with its rational accountability. To provide a structured foundation, the paper draws on Basic Formal Ontology (BFO) and other applied ontological methods to differentiate various senses of love. This work engages with objections to the understanding of love as concatenation, particularly concerning the relationship between sensation and judgment. A causal correlation model is defended, ensuring that the affective and cognitive components are linked. By offering a precise and scalable ontological account, this work lays the foundation for future interdisciplinary applications, making love a subject of formal inquiry in ontology engineering, artificial intelligence, and the sciences.
Mean-field stochastic differential equations, also called McKean--Vlasov equations, are the limiting equations of interacting particle systems with fully symmetric interaction potential. Such systems play an important role in a variety of fields ranging from biology and physics to sociology and economics. Global information about the behavior of complex dynamical systems can be obtained by analyzing the eigenvalues and eigenfunctions of associated transfer operators such as the Perron--Frobenius operator and the Koopman operator. In this paper, we extend transfer operator theory to McKean--Vlasov equations and show how extended dynamic mode decomposition and the Galerkin projection methodology can be used to compute finite-dimensional approximations of these operators, which allows us to compute spectral properties and thus to identify slowly evolving spatiotemporal patterns or to detect metastable sets. The results will be illustrated with the aid of several guiding examples and benchmark problems including the Cormier model, the Kuramoto model, and a three-dimensional generalization of the Kuramoto model.
Jan Philip Wahle, Krishnapriya Vishnubhotla, Bela Gipp
et al.
Work in Computational Affective Science and Computational Social Science explores a wide variety of research questions about people, emotions, behavior, and health. Such work often relies on language data that is first labeled with relevant information, such as the use of emotion words or the age of the speaker. Although many resources and algorithms exist to enable this type of labeling, discovering, accessing, and using them remains a substantial impediment, particularly for practitioners outside of computer science. Here, we present the ABCDE dataset (Affect, Body, Cognition, Demographics, and Emotion), a large-scale collection of over 400 million text utterances drawn from social media, blogs, books, and AI-generated sources. The dataset is annotated with a wide range of features relevant to computational affective and social science. ABCDE facilitates interdisciplinary research across numerous fields, including affective science, cognitive science, the digital humanities, sociology, political science, and computational linguistics.
Multi-layer networks arise naturally in various domains including biology, finance and sociology, among others. The multi-layer stochastic block model (multi-layer SBM) is commonly used for community detection in the multi-layer networks. Most of current literature focuses on statistical consistency of community detection methods under multi-layer SBMs. However, the asymptotic distributional properties are also indispensable which play an important role in statistical inference. In this work, we aim to study the estimation and asymptotic properties of the layer-wise scaled connectivity matrices in the multi-layer SBMs. We develop a novel and efficient method to estimate the scaled connectivity matrices. Under the multi-layer SBM and its variant multi-layer degree-corrected SBM, we establish the asymptotic normality of the estimated matrices under mild conditions, which can be used for interval estimation and hypothesis testing. Simulations show the superior performance of proposed method over existing methods in two considered statistical inference tasks. We also apply the method to a real dataset and obtain interpretable results.
Fatemeh Alizadeh, Dave Randall, Peter Tolmie
et al.
The evolution of smart home technologies, particularly agentic ones such as conversational agents, robots, and virtual avatars, is reshaping our understanding of home and domestic life. This shift highlights the complexities of modern domestic life, with the household landscape now featuring diverse cohabiting units like co-housing and communal living arrangements. These agentic technologies present specific design challenges and opportunities as they become integrated into everyday routines and activities. Our workshop envisions smart homes as dynamic, user-shaped spaces, focusing on the integration of these technologies into daily life. We aim to explore how these technologies transform household dynamics, especially through boundary fluidity, by uniting researchers and practitioners from fields such as design, sociology, and ethnography. Together, we will develop a taxonomy of challenges and opportunities, providing a structured perspective on the integration of agentic technologies and their impact on contemporary living arrangements.
In many scientific fields such as biology, psychology and sociology, there is an increasing interest in estimating the causal effect of a matrix exposure on an outcome. Covariate balancing is crucial in causal inference and both exact balancing and approximate balancing methods have been proposed in the past decades. However, due to the large number of constraints, it is difficult to achieve exact balance or to select the threshold parameters for approximate balancing methods when the treatment is a matrix. To meet these challenges, we propose the weighted Euclidean balancing method, which approximately balance covariates from an overall perspective. This method is also applicable to high-dimensional covariates scenario. Both parametric and nonparametric methods are proposed to estimate the causal effect of matrix treatment and theoretical properties of the two estimations are provided. Furthermore, the simulation results show that the proposed method outperforms other methods in various cases. Finally, the method is applied to investigating the causal relationship between children's participation in various training courses and their IQ. The results show that the duration of attending hands-on practice courses for children at 6-9 years old has a siginificantly positive impact on children's IQ.
Ten years after the collapse of the Rana Plaza textile factory in Dhaka, Bangladesh that killed over $1\,000$ factory workers, the event has become a symbol for the desolate working conditions in fast fashion producer countries in the global south. We analyze the global Twitter discourse on this event over a three week window around the collapse date over the years $2013$ to $2022$ by a mixture of network-theoretic quantitative and discourse-theoretic qualitative methods. In particular, key communicators and the community structure of the discourse participants are identified using a multilayer network modeling approach and the interpretative patterns of the key communicator's tweets of all years are analyzed using the sociology of knowledge approach to discourse. This combination of quantitative and qualitative methods reveals that the discourse is separated into three phases: reporting, reprocessing, and commemoration. These phases can be identified by the temporal evolution, network-structural properties, and the contentual analysis of the discourse. After the negotiation of the interpretative framework in the reprocessing phase, subsequent years are characterized by its commemorative repetition as well as resulting demands by different international actor groups despite highly fluctuating participants.
With ubiquitous exposure of AI systems today, we believe AI development requires crucial considerations to be deemed trustworthy. While the potential of AI systems is bountiful, though, is still unknown-as are their risks. In this work, we offer a brief, high-level overview of societal impacts of AI systems. To do so, we highlight the requirement of multi-disciplinary governance and convergence throughout its lifecycle via critical systemic examinations (e.g., energy consumption), and later discuss induced effects on the environment (i.e., carbon footprint) and its users (i.e., social development). In particular, we consider these impacts from a multi-disciplinary perspective: computer science, sociology, environmental science, and so on to discuss its inter-connected societal risks and inability to simultaneously satisfy aspects of well-being. Therefore, we accentuate the necessity of holistically addressing pressing concerns of AI systems from a socioethical impact assessment perspective to explicate its harmful societal effects to truly enable humanity-centered Trustworthy AI.
This paper examines the emerging circular economy trends in universities in the United Arab Emirates (UAE). The circular economy is a model that aims to reduce waste and maximize the use of resources, promoting sustainable development. The study analyses UAE universities' various initiatives to adopt circular economy practices, including using renewable energy, sustainable building design, and waste reduction strategies. The paper also discusses the challenges and opportunities for implementing circular economy practices in universities in the UAE and highlights examples of circular economy initiatives in various universities. The paper concludes by providing recommendations for universities in the UAE to promote sustainable practices further and contribute to the circular economy movement. The findings of this study provide insights into the emerging circular economy trends in universities in the UAE and offer directions for future research in this area. The ADKAR change management can be adapted to inspire the CE initiatives of the UAE Education sector.