Hasil untuk "Social sciences (General)"

Nur Fariha Amir, Aslizah Mohd-Aris, Tuan Norhafizah Tuan-Zakaria et al.

Background: Volvariella volvacea is a highly nutritious edible mushroom grown mainly in Southeast Asian countries. However, the low yield of V. volvacea has discouraged farmers from engaging in its production. Objective: The study was conducted to observe the improvement of V. volvacea yield depending on various physiochemical parameters of V. volvacea growth. Methods: The parameters tested in this study include the weight of the substrate, i.e., 2 kg (W1) and 6 kg (W2); the surface area of the substrate: A1 (1218 cm2), A2 (1530 cm2) and A3 (2000 cm2); and four different substrate formulations (F1, F2, F3 and F4). Results: Substrate weight and surface area were found to be important, but not critical, factors in determining fruiting bodies formation, total fungal mass, and BE rate. However, the formulation media showed a significant contribution that could help in the induction of fruiting bodies. According to the results, the culture medium with a mixture of EFB substrate and black soil showed the highest BE percentage of 17.75 % (at optimised substrate weights = 2 kg). Conclusion: The results of this study can be used as a reference for further studies to improve the cultivation of V. volvacea, especially when EFB fibres are used as the main substrate. Future studies to identify genes involved in the formation of fruiting bodies are strongly recommended.

Dragan Filimonovic, Christian Rutzer, Conny Wunsch

This paper estimates the effect of Generative AI (GenAI) adoption on scientific productivity and quality in the social and behavioral sciences. Using matched author-level panel data and a difference-in-differences design, we find that GenAI adoption is associated with sizable increases in research productivity, measured by the number of published papers. It also leads to moderate gains in publication quality, based on journal impact factors. These effects are most pronounced among early-career researchers, authors working in technically complex subfields, and those from non-English-speaking countries. The results suggest that GenAI tools may help lower some structural barriers in academic publishing and promote more inclusive participation in research.

Steph Grohmann

In biomedical science, review by a Research Ethics Committee (REC) is an indispensable way of protecting human subjects from harm. However, in social science and the humanities, mandatory ethics compliance has long been met with scepticism as biomedical models of ethics can map poorly onto methodologies involving complex socio-political and cultural considerations. As a result, tailored ethics training and support as well as access to RECs with the necessary expertise is lacking in some areas, including parts of Europe and low- and middle-income countries. This paper suggests that Generative AI can meaningfully contribute to closing these gaps, illustrating this claim by presenting EthicAlly, a proof-of-concept prototype for an AI-powered ethics support system for social science and humanities researchers. Drawing on constitutional AI technology and a collaborative prompt development methodology, EthicAlly provides structured ethics assessment that incorporates both universal ethics principles and contextual and interpretive considerations relevant to most social science research. In supporting researchers in ethical research design and preparation for REC submission, this kind of system can also contribute to easing the burden on institutional RECs, without attempting to automate or replace human ethical oversight.

Joseph B. Bak-Coleman, Stephan Lewandowsky, Philipp Lorenz-Spreen et al.

Social media is nearly ubiquitous in modern life, raising concerns about its societal impacts-from mental health and polarization to violence and democratic disruption. Yet research on its causal effects remains inconclusive: observational studies often find concerning associations, while randomized controlled trials (RCTs) tend to yield small, conflicting, or null results. Literature summaries tend to causally prioritize findings from RCTs, often arguing that concerns about social media are overstated. However, like observational studies, RCTs rely on assumptions that can easily be violated in the context of social media, especially regarding societal outcomes at scale. Here, we enumerate and examine the features of social media as a complex system that challenge our ability to infer causality at societal scales. Drawing on insight from disciplines that have faced similar challenges, like climate-science or epidemiology, we propose a path forward that combines the strength of observational and experimental approaches while acknowledging the limitations of each.

Zeyi Zhang, Yanju Zhou, Heyuan Yao et al.

We present Social Agent, a novel framework for synthesizing realistic and contextually appropriate co-speech nonverbal behaviors in dyadic conversations. In this framework, we develop an agentic system driven by a Large Language Model (LLM) to direct the conversation flow and determine appropriate interactive behaviors for both participants. Additionally, we propose a novel dual-person gesture generation model based on an auto-regressive diffusion model, which synthesizes coordinated motions from speech signals. The output of the agentic system is translated into high-level guidance for the gesture generator, resulting in realistic movement at both the behavioral and motion levels. Furthermore, the agentic system periodically examines the movements of interlocutors and infers their intentions, forming a continuous feedback loop that enables dynamic and responsive interactions between the two participants. User studies and quantitative evaluations show that our model significantly improves the quality of dyadic interactions, producing natural, synchronized nonverbal behaviors.

Gian Marco Orlando, Valerio La Gatta, Diego Russo et al.

Generative Agent-Based Modeling (GABM) is an emerging simulation paradigm that combines the reasoning abilities of Large Language Models with traditional Agent-Based Modeling to replicate complex social behaviors, including interactions on social media. While prior work has focused on localized phenomena such as opinion formation and information spread, its potential to capture global network dynamics remains underexplored. This paper addresses this gap by analyzing GABM-based social media simulations through the lens of the Friendship Paradox (FP), a counterintuitive phenomenon where individuals, on average, have fewer friends than their friends. We propose a GABM framework for social media simulations, featuring generative agents that emulate real users with distinct personalities and interests. Using Twitter datasets on the US 2020 Election and the QAnon conspiracy, we show that the FP emerges naturally in GABM simulations. Consistent with real-world observations, the simulations unveil a hierarchical structure, where agents preferentially connect with others displaying higher activity or influence. Additionally, we find that infrequent connections primarily drive the FP, reflecting patterns in real networks. These findings validate GABM as a robust tool for modeling global social media phenomena and highlight its potential for advancing social science by enabling nuanced analysis of user behavior.

Xiaohua Wu, Xiaohui Tao, Wenjie Wu et al.

Social surveys in computational social science are well-designed by elaborate domain theories that can effectively reflect the interviewee's deep thoughts without concealing their true feelings. The candidate questionnaire options highly depend on the interviewee's previous answer, which results in the complexity of social survey analysis, the time, and the expertise required. The ability of large language models (LLMs) to perform complex reasoning is well-enhanced by prompting learning such as Chain-of-thought (CoT) but still confined to left-to-right decision-making processes or limited paths during inference. This means they can fall short in problems that require exploration and uncertainty searching. In response, a novel large language model prompting method, called Random Forest of Thoughts (RFoT), is proposed for generating uncertainty reasoning to fit the area of computational social science. The RFoT allows LLMs to perform deliberate decision-making by generating diverse thought space and randomly selecting the sub-thoughts to build the forest of thoughts. It can extend the exploration and prediction of overall performance, benefiting from the extensive research space of response. The method is applied to optimize computational social science analysis on two datasets covering a spectrum of social survey analysis problems. Our experiments show that RFoT significantly enhances language models' abilities on two novel social survey analysis problems requiring non-trivial reasoning.

Dachun Sun, You Lyu, Jinning Li et al.

This paper introduces SCRAG, a prediction framework inspired by social computing, designed to forecast community responses to real or hypothetical social media posts. SCRAG can be used by public relations specialists (e.g., to craft messaging in ways that avoid unintended misinterpretations) or public figures and influencers (e.g., to anticipate social responses), among other applications related to public sentiment prediction, crisis management, and social what-if analysis. While large language models (LLMs) have achieved remarkable success in generating coherent and contextually rich text, their reliance on static training data and susceptibility to hallucinations limit their effectiveness at response forecasting in dynamic social media environments. SCRAG overcomes these challenges by integrating LLMs with a Retrieval-Augmented Generation (RAG) technique rooted in social computing. Specifically, our framework retrieves (i) historical responses from the target community to capture their ideological, semantic, and emotional makeup, and (ii) external knowledge from sources such as news articles to inject time-sensitive context. This information is then jointly used to forecast the responses of the target community to new posts or narratives. Extensive experiments across six scenarios on the X platform (formerly Twitter), tested with various embedding models and LLMs, demonstrate over 10% improvements on average in key evaluation metrics. A concrete example further shows its effectiveness in capturing diverse ideologies and nuances. Our work provides a social computing tool for applications where accurate and concrete insights into community responses are crucial.

Anne Greve, Øystein Skundberg, Solveig Østrem

Artikkelen omhandler barnehagens hverdagsliv under covid-19-pandemien. Oppmerksomheten rettes mot hvordan barna ble berørt av de nasjonale smitteverntiltakene barnehagene ble pålagt. Det empiriske materialet som ligger til grunn for artikkelen, er dagboknotater fra barnehageansatte nedtegnet i april, mai og juni 2020. I diskusjonen av de empiriske funnene trekker vi veksler på Foucaults teori om biopolitikk, der hygiene betraktes som verktøy for sosial kontroll, og Nadesans forståelse av biopolitikk-begrepet, der barndom og oppvekst vies spesiell oppmerksomhet. Smitteverntiltakene i barnehagene ble innført «ovenfra», men måtte forvaltes og praktiseres «nedenfra». Resultatene viser at tiltakene både var disiplinerende (fjerning av leker, forbud mot å leke med bestemte andre) og selvdisiplinerende (barna ble opplært til å passe på egen og andres håndvask). Vi ser også hvordan en institusjon som er regulert i tid og rom, under pandemien i enda større grad ble regulert gjennom krav om «redusert kontakthyppighet» og segregering av barnegruppene. Tiltak som var motivert ut fra helse og sikkerhet for alle landets innbyggere, satte preg på barns hverdagsliv både gjennom konkrete begrensninger og ved at de måtte forholde seg til ekstraordinære hygienekrav og frykt for smitte. English abstract Infection Control, Biopolitics and Early Childhood Education and Care. How Children Were Directly Affected by Infection Prevention Measures in Kindergartens During Spring 2020 The article concerns everyday life in Early Childhood Education and Care (ECEC) institutions during the COVID-19 pandemic. Attention is focused on how children were affected by the infection control measures that kindergartens were required to implement. The empirical data that forms the basis of the article are diary notes from kindergarten staff recorded in April, May, and June 2020. In the discussion of the empirical findings, we draw on Foucault’s theory of biopolitics, where hygiene is considered as a tool for social control. The infection control measures in the kindergartens were introduced “from above”, but had to be managed and practiced “from below”. The measures were both about discipline (removal of toys, restrictions on allowed playmates) and dissemination of knowledge and self-discipline (the children were taught to take responsibility for their own hand-washing, as well as that of the other children). We also consider how an institution regulated in time and space was regulated to an even greater extent during the pandemic through requirements for reduced frequency of contact and segregation of smaller groups of children. Measures motivated by the protection of public health and safety affected children’s everyday lives, both through specific restrictions and due to the fact that they had to deal with extraordinary hygiene requirements and fear of infection.

Yu. P. Voronov

The article explores the process of creating a large cooperative housing complex in a district of New York. It highlights the unique circumstances that made the cooperative City project possible in the United States. The article also examines the efforts of European countries to foster urban residents’ involvement in urban development. It provides examples of innovative solutions implemented by the population of various European cities. The article delves into the Russian experience of utilizing public initiatives to enhance urban development and improve the quality of life. It emphasizes that the promotion of cooperation in urban life is supported by both governmental authorities and individual citizens and local communities. The article highlights a new phase in this process — changes in urban planning, with the transition from general to master plans becoming part of federal policy.

Nguyen Hong Thuy

In preschool activities, children's need for cooperation is extremely strong. In today's era, the knowledge economy along with the scientific and technological revolution and international integration are developing rapidly. That requires children to know how to cooperate, share, listen, resolve conflicts and coordinate with each other. Therefore, cooperation skills are an indispensable factor to help children succeed. The article studies the reality of cooperative skills education management in Thai Nguyen city preschools in terms of planning, organizing and directing implementation, thereby proposing solutions such as: Organize training for teachers on cooperative skills education methods for 5-6 year old children in preschool; Mobilize forces inside and outside the school to participate in educating cooperation skills for 5-6 year old children in preschool; Mobilizing forces inside and outside of the school; Development of facilities to serve cooperative skills education activities for 5-6 year old children in preschool.

Flor Ivett Reyes Guillén, Bárbara Muñoz Alonso Reyes

El presente estudio se planteó en corte mixto, transversal y analítico. Su objetivo principal fue identificar la presencia de estrés y depresión, además de los Recursos de Afrontamiento (RA) de universitarios después del confinamiento por COVID-19. Los recursos de afrontamiento nos permiten adaptarnos a las diferentes situaciones del ambiente; son procesos cognitivos y conductuales elementales como respuesta ante la presencia de estrés (Lazarus y Folkman, 1986), este último, ha estado presente en jóvenes universitarios por diversas causas pero ¿de qué formas se ha presentado post confinamiento? Los resultados obtenidos por una muestra de 220 estudiantes arrojan una mayor presencia de estrés percibido (PE=2.404) que de percepción del control del estrés (PCE=2.378), además de la presencia de depresión leve (BDI-2=8.1); se identificó una correcta percepción de RA; pero un débil uso de estos. Únicamente el estrés encuentra evidencia de relación con el confinamiento.

Sriniwas Pandey, Yiding Cao, Yingjun Dong et al.

Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label "eccentric" ideas. The eccentricity of any idea is defined as the deviation of that idea from the norm of the social neighborhood. We collected and analyzed data from two completely different sources: public social media and online experiments in a controlled environment. We compared the popularity of ideas against their eccentricity to understand individuals' fascination towards eccentricity. We found that more eccentric ideas have a higher probability of getting a greater number of "likes". Additionally, we demonstrate that the social neighborhood of an individual conceals eccentricity changes in one's own opinions and facilitates generation of eccentric ideas at a collective level.

Karina Shyrokykh, Maksym Girnyk, Lisa Dellmuth

To analyse large numbers of texts, social science researchers are increasingly confronting the challenge of text classification. When manual labeling is not possible and researchers have to find automatized ways to classify texts, computer science provides a useful toolbox of machine-learning methods whose performance remains understudied in the social sciences. In this article, we compare the performance of the most widely used text classifiers by applying them to a typical research scenario in social science research: a relatively small labeled dataset with infrequent occurrence of categories of interest, which is a part of a large unlabeled dataset. As an example case, we look at Twitter communication regarding climate change, a topic of increasing scholarly interest in interdisciplinary social science research. Using a novel dataset including 5,750 tweets from various international organizations regarding the highly ambiguous concept of climate change, we evaluate the performance of methods in automatically classifying tweets based on whether they are about climate change or not. In this context, we highlight two main findings. First, supervised machine-learning methods perform better than state-of-the-art lexicons, in particular as class balance increases. Second, traditional machine-learning methods, such as logistic regression and random forest, perform similarly to sophisticated deep-learning methods, whilst requiring much less training time and computational resources. The results have important implications for the analysis of short texts in social science research.

Bushi Xiao, Ziyuan Yin, Zixuan Shan

This article proposes a social simulation paradigm based on the GPT-3.5 large language model. It involves constructing Generative Agents that emulate human cognition, memory, and decision-making frameworks, along with establishing a virtual social system capable of stable operation and an insertion mechanism for standardized public events. The project focuses on simulating a township water pollution incident, enabling the comprehensive examination of a virtual government's response to a specific public administration event. Controlled variable experiments demonstrate that the stored memory in generative agents significantly influences both individual decision-making and social networks. The Generative Agent-Based Simulation System introduces a novel approach to social science and public administration research. Agents exhibit personalized customization, and public events are seamlessly incorporated through natural language processing. Its high flexibility and extensive social interaction render it highly applicable in social science investigations. The system effectively reduces the complexity associated with building intricate social simulations while enhancing its interpretability.

Julie Jiang, Emilio Ferrara

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation methods struggle with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed to address these challenges. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science.

Stanojević Petar D.

The paper describes and illustrates a simple, robust, yet sufficiently detailed model that ensures quick and accurate enough forecast of trends and their possible effect on the processes related to energy security and, accordingly, global security. The heuristic model also takes into account diversity in the understanding of the energy paradigm. It is based on the 4As definition of energy security (Acceptability, Availability, Affordability, Accessibility) and uses the regional security complex theory. It considers four factors of the first and twenty-three factors of the second level. The created model enables considering the factors that are difficult to measure, which makes the forecast comprehensive. The model is applied in the cases of the EU, Serbia and China. The conclusion is that a set of factors acting towards the reduction of energy security of the countries whose strategic aspiration is the "green paradigm" exceeds by far the number of those with positive effects. The results point to the conclusion that China should continue the current energy strategy. The actual application of the model, through the analysis of influential factors, indicates the problems that will have their security-related repercussions. The factors that need to be treated as priorities have been singled out if the actual situation needs improvement for the purpose of increasing and maintaining energy security of a country or a regional complex.

Fujun Hou

I present an example in which the individuals' preferences are strict orderings, and under the majority rule, a transitive social ordering can be obtained and thus a non-empty choice set can also be obtained. However, the individuals' preferences in that example do not satisfy any conditions (restrictions) of which at least one is required by Inada (1969) for social preference transitivity under the majority rule. Moreover, the considered individuals' preferences satisfy none of the conditions of value restriction (VR), extremal restriction (ER) or limited agreement (LA), some of which is required by Sen and Pattanaik (1969) for the existence of a non-empty social choice set. Therefore, the example is an exception to a number of theorems of social preference transitivity and social choice set existence under the majority rule. This observation indicates that the collection of the conditions listed by Inada (1969) is not as complete as might be supposed. This is also the case for the collection of conditions VR, ER and LA considered by Sen and Pattanaik (1969). This observation is a challenge to some necessary conditions in the current social choice theory. In addition to seeking new conditions, one possible way to deal with this challenge may be, from a theoretical prospective, to represent the identified conditions (such as the VR, ER and LA) in terms of a common mathematical tool, and then, people may find more.

Alexander C. Nwala, Alessandro Flammini, Filippo Menczer

Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a general language for modeling social media account behavior. Words in this language, called BLOC, consist of symbols drawn from distinct alphabets representing user actions and content. The language is highly flexible and can be applied to model a broad spectrum of legitimate and suspicious online behaviors without extensive fine-tuning. Using BLOC to represent the behaviors of Twitter accounts, we achieve performance comparable to or better than state-of-the-art methods in the detection of social bots and coordinated inauthentic behavior.

Joshua Zonca, Anna Folso, Alessandra Sciutti

Taking advice from others requires confidence in their competence. This is important for interaction with peers, but also for collaboration with social robots and artificial agents. Nonetheless, we do not always have access to information about others' competence or performance. In these uncertain environments, do our prior beliefs about the nature and the competence of our interacting partners modulate our willingness to rely on their judgments? In a joint perceptual decision making task, participants made perceptual judgments and observed the simulated estimates of either a human participant, a social humanoid robot or a computer. Then they could modify their estimates based on this feedback. Results show participants' belief about the nature of their partner biased their compliance with its judgments: participants were more influenced by the social robot than human and computer partners. This difference emerged strongly at the very beginning of the task and decreased with repeated exposure to empirical feedback on the partner's responses, disclosing the role of prior beliefs in social influence under uncertainty. Furthermore, the results of our functional task suggest an important difference between human-human and human-robot interaction in the absence of overt socially relevant signal from the partner: the former is modulated by social normative mechanisms, whereas the latter is guided by purely informational mechanisms linked to the perceived competence of the partner.