Arafatur Rahaman, Abidur Rahman Efaz, Mushfiq Ali Rajon
et al.
Abstract The Bangladesh Uprising of July 2024 was a critical period in the country’s political trajectory, synonymous with mass mobilization, regime overthrow, and an unprecedented information blackout. This research explores the pivotal contribution of digital disinformation to the course and perceptions of the revolution. To that end, in addition to a quantitative content and sentiment analysis, we combine this with qualitative interviews, looking specifically at how misinformation was shared, amplified, and debunked across digital platforms. This study draws on 112 verified fact-checking reports, 87 news articles, and 15 semi-structured interviews with journalists, fact-checkers, and digital rights activists to examine how politically charged disinformation shaped the July 2024 protests in Bangladesh. Findings reveal a surge in coordinated campaigns amplified through social media echo chambers, influencing protest narratives and public trust. Although local fact-checking grew rapidly, structural challenges of limited resources limited their effects, which worsened with algorithm amplifications, low media literacy, and partisan mistrust. The article locates these dynamics of disinformation in the context of more general problems of media regulation, political polarization, and digital infrastructures in transitional democracies. It calls for a multi-pronged solution, including institutional reform, algorithmic accountability, and continued digital literacy investments. Through the lens of Bangladesh’s July 2024 crisis, the study adds to the global conversation on disinformation in crony-authoritarianism and underscores the critical nature of context-sensitive verification systems in the Global South.
In 1994, three articles from the Pétainist magazine L'Unité française were discovered by chance. They were allegedly written by Paul Ricoeur in 1941-1942. This sparked a debate in which Ricoeur himself spoke up. Criticism of Ricoeur intensified after the publication of La mémoire, l'histoire, l'oubli. Raynova reconstructs the various positions to demonstrate the extent to which Ricoeur took responsibility for his past, as well as to show that some of the accusations against him were unfounded. She also emphasizes that everyone is responsible for what they write about others and that calumny and self-righteousness cannot be justified as a "right to one's own opinion." Rather, they should be condemned in the spirit of Levinas.
Drawing from studies of affect, this article explores how affect theory can inform how we theorise schedules. The notion of the schedule, of great interest to sociologists, has mostly been explored from a constructionist approach. The current article extends these readings, proposing the concepts of ‘scheduling in motion’ and ‘affective scheduling’, through which scheduling is explored as a relational and affective process. In doing so, it positions affective scheduling as a mode of inquiry that embraces the multiplicity and fragmentation of lived time. From this vantage point, I highlight how scheduling is felt, sensed and materialised in ways that bypass realms of demarcated temporal patterns. Important in this respect is the understanding that scheduling is a grouping together of heterogeneous elements, emerging in moments of encounters between bodies. This study is a first step to addressing the potential of affective scheduling in exploring everyday lived experiences, temporality and affection.
Agneta Blomberg, Gunnel Hensing, Monica Bertilsson
et al.
Abstract Background Mental health problems are common in the working-age population. More knowledge is needed on how to support work participation and reduce sickness absence. The objective of the study was to estimate the distribution of mental well-being and work capacity in women and men in a working population and assess the association between mental well-being and work capacity, while adjusting for sociodemographic characteristics, health status, and working positions. Methods Cross-sectional data were collected through an online survey distributed to individuals who were currently working. The study population consisted of 8462 employees (58% women). The WHO-5 Mental Well-being Index (scale ranging from 0 to 100 with higher scores representing a better mental well-being) and the Capacity to Work Instrument (C2WI) (scale ranging from 14 to 56 with higher scores representing a more strained work capacity) were used. Univariable and multivariable linear regressions were used to assess the associations between self-perceived mental well-being and capacity to work, adjusting for sociodemographic characteristics, health status, and working positions. Results Low self-perceived mental well-being and strained work capacity were more common among women, particularly younger aged (18–34 years). Poor health status was associated with strained work capacity in both men and women. Regression analyses showed that lower self-perceived mental well-being was significantly associated with strained work capacity. Among women, the fully adjusted model showed a regression coefficient (B) of − 0.253 (95% CI: −0.264 to − 0.242); among men, it was − 0.225 (95% CI: −0.237 to − 0.213). Conclusions This study, focusing on a currently working population, identified disparities in self-perceived mental well-being and work capacity across gender and age groups. These findings underscore the importance of early workplace interventions to support mental well-being and work capacity in these sub-groups. Notably, the association between the WHO-5 and C2WI may be partly attributable to item-level overlap, as certain C2WI items may capture symptoms related to mental health. This potential overlap should be considered when interpreting the findings.
Yi-Hsuan Lin, Lalitphat Khongsomchit, Sakdirat Kaewunruen
et al.
IntroductionBuilding Information Modelling (BIM) has emerged as a multidisciplinary methodology that integrates information-rich data with virtual representations to support the management of built assets throughout their lifecycle. While BIM is increasingly adopted in architecture, engineering, and construction (AEC) industries and demonstrates significant value in infrastructure projects; however, its application in the railway sector remains limited. The complexity of railway networks, combined with the growing demand for transit projects, presents unique challenges that hinder effective implementation.MethodsThis study investigates the barriers of BIM adoption within the railway industry through a structured questionnaire distributed to professionals and a subsequent detailed analysis of responses.ResultsThis study identifies critical gaps in current BIM practices and highlights several severe obstacles that require urgent attention. Feedback reveals key challenges across four main areas: (1) Technology, (2) Market, (3) Socio-cultural factors, and (4) Policy.DiscussionBy outlining these barriers and suggesting potential solutions, the study provides valuable insights for stakeholders and identifies future research directions to advance BIM integration in railway projects.
Engineering (General). Civil engineering (General), City planning
Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind spot for understanding distributed cognition. This paper introduces "Cognitive Infrastructure Studies" (CIS) as a new interdisciplinary domain to reconceptualize AI as "cognitive infrastructures": foundational, often invisible systems conditioning what is knowable and actionable in digital societies. These semantic infrastructures transport meaning, operate through anticipatory personalization, and exhibit adaptive invisibility, making their influence difficult to detect. Critically, they automate "relevance judgment," shifting the "locus of epistemic agency" to non-human systems. Through narrative scenarios spanning individual (cognitive dependency), collective (democratic deliberation), and societal (governance) scales, we describe how cognitive infrastructures reshape human cognition, public reasoning, and social epistemologies. CIS aims to address how AI preprocessing reshapes distributed cognition across individual, collective, and cultural scales, requiring unprecedented integration of diverse disciplinary methods. The framework also addresses critical gaps across disciplines: cognitive science lacks population-scale preprocessing analysis capabilities, digital sociology cannot access individual cognitive mechanisms, and computational approaches miss cultural transmission dynamics. To achieve this goal CIS also provides methodological innovations for studying invisible algorithmic influence: "infrastructure breakdown methodologies", experimental approaches that reveal cognitive dependencies by systematically withdrawing AI preprocessing after periods of habituation.
The worlds of Data Science (including big and/or federated data, machine learning, etc) and Astrophysics started merging almost two decades ago. For instance, around 2005, international initiatives such as the Virtual Observatory framework rose to standardize the way we publish and transfer data, enabling new tools such as VOSA (SED Virtual Observatory Analyzer) to come to existence and remain relevant today. More recently, new facilities like the Vera Rubin Observatory, serve as motivation to develop efficient and extremely fast (very often deep learning based) methodologies in order to fully exploit the informational content of the vast Legacy Survey of Space and Time (LSST) dataset. However, fundamental changes in the way we explore and analyze data cannot permeate in the "astrophysical sociology and idiosyncrasy" without adequate training. In this talk, I will focus on one specific initiative that has been extremely successful and is based on "learning by doing": the La Serena School for Data Science. I will also briefly touch on a different successful approach: a series of schools organized by the Spanish Virtual Observatory. The common denominator among the two kinds of schools is to present the students with real scientific problems that benefit from the concepts / methodologies taught. On the other hand, the demographics targeted by both initiatives vary significantly and can represent examples of two "flavours" to be followed by others.
Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc . Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner.
As users increasingly seek guidance from LLMs for decision-making in daily life, many of these decisions are not clear-cut and depend significantly on the personal values and ethical standards of people. We present DailyDilemmas, a dataset of 1,360 moral dilemmas encountered in everyday life. Each dilemma presents two possible actions, along with affected parties and relevant human values for each action. Based on these dilemmas, we gather a repository of human values covering diverse everyday topics, such as interpersonal relationships, workplace, and environmental issues. With DailyDilemmas, we evaluate LLMs on these dilemmas to determine what action they will choose and the values represented by these action choices. Then, we analyze values through the lens of five theoretical frameworks inspired by sociology, psychology, and philosophy, including the World Values Survey, Moral Foundations Theory, Maslow's Hierarchy of Needs, Aristotle's Virtues, and Plutchik's Wheel of Emotions. For instance, we find LLMs are most aligned with self-expression over survival in World Values Survey and care over loyalty in Moral Foundations Theory. Interestingly, we find substantial preference differences in models for some core values. For example, for truthfulness, Mixtral-8x7B neglects it by 9.7% while GPT-4-turbo selects it by 9.4%. We also study the recent guidance released by OpenAI (ModelSpec), and Anthropic (Constitutional AI) to understand how their designated principles reflect their models' actual value prioritization when facing nuanced moral reasoning in daily-life settings. Finally, we find that end users cannot effectively steer such prioritization using system prompts.
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal
et al.
Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of provided datasets? To evaluate this question, we present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. The benchmark is designed to systematically assess current model capabilities in discovery tasks and provide a useful resource for improving them. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering, by manually deriving discovery workflows from published papers to approximate the real-world challenges faced by researchers, where each task is defined by a dataset, its metadata, and a discovery goal in natural language. We additionally provide 903 synthetic tasks to conduct controlled evaluations across task complexity. Furthermore, our structured formalism of data-driven discovery enables a facet-based evaluation that provides useful insights into different failure modes. We evaluate several popular LLM-based reasoning frameworks using both open and closed LLMs as baselines on DiscoveryBench and find that even the best system scores only 25%. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
Elizandra Garcia da Silva, Verônica Toledo Ferreira de Carvalho, Renato Machado Saldanha
O modo de produção capitalista, ainda imerso na crise de 2007/2008, presencia o agravamento dessa crise com a pandemia da COVID-19. Buscando saídas para a retomada do crescimento de seus níveis de acumulação, amplia e aprofunda as formas de exploração do trabalho da classe trabalhadora. Essas mudanças no trabalho trouxeram implicações no tempo livre e, em particular, no lazer. A partir dos pressupostos teórico-metodológicos do materialismo histórico-dialético e por meio da pesquisa bibliográca, realizamos as análises e consideramos que, ao Estado brasileiro, neoliberal, não é prioritária a garantia
de políticas públicas de lazer, restringindo ainda mais as possibilidades de lazer da classe trabalhadora. Enquanto a burguesia segue se fartando do consumo das mais variadas mercadorias de lazer, aos trabalhadores seguirão restando apenas migalhas caídas da mesa, até que sejam capazes de revolucionar esse modo de produção, emancipando-se e forjando o socialismo.
Kaitlin Rasmussen, Jocelyne Chen, Rebecca L. Colquhoun
et al.
Gender inequity is one of the biggest challenges facing the STEM workforce. While there are many studies that look into gender disparities within STEM and academia, the majority of these have been designed and executed by those unfamiliar with research in sociology and gender studies. They adopt a normative view of gender as a binary choice of 'male' or 'female,' leaving individuals whose genders do not fit within that model out of such research entirely. This especially impacts those experiencing multiple axes of marginalization, such as race, disability, and socioeconomic status. For STEM fields to recruit and retain members of historically excluded groups, a new paradigm must be developed. Here, we collate a new dataset of the methods used in 119 past studies of gender equity, and recommend better survey practices and institutional policies based on a more complex and accurate approach to gender. We find that problematic approaches to gender in surveys can be classified into 5 main themes - treating gender as white, observable, discrete, as a statistic, and as inconsequential. We recommend allowing self-reporting of gender and never automating gender assignment within research. This work identifies the key areas of development for studies of gender-based inclusion within STEM, and provides recommended solutions to support the methodological uplift required for this work to be both scientifically sound and fully inclusive.
Det är återigen dags för Sociologisk Forskning att få nya redaktörer och en ny redaktion. Vid årsskiftet har sociologiämnet vid Södertörns högskola haft ansvar för tidskriften under tre år, och det är snart tid för Sociologförbundet att besluta om vid vilket lärosäte tidskriften ska vara placerad de kommande åren.
Förbundet vill härmed uppmana våra medlemsinstitutioner att senast den 15 september inkomma med anmälan om intresse för att ansvara för Sociologisk Forskning under perioden 2023–2024, en tvåårsperiod som kan förlängas till fyra år. Vi beskriver här i korthet vad ansvaret innebär, vad ni bör tänka på när ni formulerar en intresseanmälan samt vilka kriterier som kommer att vara centrala när vi bestämmer ny hemvist för Sociologisk Forskning.
The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.
Aggregated relational data (ARD), formed from "How many X's do you know?" questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive as it does not require a sample from the target population and does not ask respondents to self-reveal their own status. This is helpful for studying hard-to-reach populations like female sex workers who may be hesitant to reveal their status. From December 2008 to February 2009, the Kiev International Institute of Sociology (KIIS) collected ARD from 10,866 respondents to estimate the size of HIV-related groups in Ukraine. To analyze this data, we propose a new ARD model which incorporates respondent and group covariates in a regression framework and includes a bias term that is correlated between groups. We also introduce a new scaling procedure utilizing the correlation structure to further reduce biases. The resulting size estimates of those most-at-risk of HIV infection can improve the HIV response efficiency in Ukraine. Additionally, the proposed model allows us to better understand two network features without the full network data: 1. What characteristics affect who respondents know, and 2. How is knowing someone from one group related to knowing people from other groups. These features can allow researchers to better recruit marginalized individuals into the prevention and treatment programs. Our proposed model and several existing NSUM models are implemented in the networkscaleup R package.
The idea of intersectionality has become a frequent topic of discussion both in academic sociology, as well as among popular movements for social justice such as Black Lives Matter, intersectional feminism, and LGBT rights. Intersectionality proposes that an individual's experience of society has aspects that are irreducible to the sum of one's various identities considered individually, but are "greater than the sum of their parts." In this work, we show that the effects of intersectional identities can be statistically observed in empirical data using information theory. We show that, when considering the predictive relationship between various identities categories such as race, sex, and income (as a proxy for class) on outcomes such as health and wellness, robust statistical synergies appear. These synergies show that there are joint-effects of identities on outcomes that are irreducible to any identity considered individually and only appear when specific categories are considered together (for example, there is a large, synergistic effect of race and sex considered jointly on income irreducible to either race or sex). We then show using synthetic data that the current gold-standard method of assessing intersectionalities in data (linear regression with multiplicative interaction coefficients) fails to disambiguate between truly synergistic, greater-than-the-sum-of-their-parts interactions, and redundant interactions. We explore the significance of these two distinct types of interactions in the context of making inferences about intersectional relationships in data and the importance of being able to reliably differentiate the two. Finally, we conclude that information theory, as a model-free framework sensitive to nonlinearities and synergies in data, is a natural method by which to explore the space of higher-order social dynamics.
Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i.e., status theory and balance theory) and conduct empirical studies on real-world datasets to analyze the social mechanism in signed directed networks. Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks. The proposed model simultaneously reconstructs link signs, link directions, and signed directed triangles. We validate our model's effectiveness on five real-world datasets, which are commonly used as the benchmark for signed network embedding. Experiments demonstrate the proposed model outperforms existing models, including feature-based methods, network embedding methods, and several GNN methods.
Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton's law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination's opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual's destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model's predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility.