Hasil untuk "Social sciences (General)"

A. Kaplan

P. Werbos

Mauro Marino-Jiménez, Norma Sánchez-Chávez, Yenny Rivero-Fortón et al.

Student performance, disciplinary innovation and teaching methodology occupy the main concerns of educational research. Therefore, there is a greater interest in gamification strategies, where digital tools facilitate the development of competitive activities and strengthening of learning. One example of this idea is the use of video games created for non-educational purposes, where disciplinary strategies and/or social skills can be developed. In this paper, the game Among Us is used to develop an educational experience at higher education. Its use helps to develop a methodology for the identification and analysis of fallacies, according to their recurrency and effectiveness. The result of this learning experience led to a greater comprehension about the use of fallacies, favorable perceptions about the use of games for educational experience, and a deeper reflection about social intelligence in the students.

Yanhui Sun, Wu Liu, Wentao Wang et al.

Understanding the intrinsic mechanisms of social platforms is an urgent demand to maintain social stability. The rise of large language models provides significant potential for social network simulations to capture attitude dynamics and reproduce collective behaviors. However, existing studies mainly focus on scaling up agent populations, neglecting the dynamic evolution of social relationships. To address this gap, we introduce DynamiX, a novel large-scale social network simulator dedicated to dynamic social network modeling. DynamiX uses a dynamic hierarchy module for selecting core agents with key characteristics at each timestep, enabling accurate alignment of real-world adaptive switching of user roles. Furthermore, we design distinct dynamic social relationship modeling strategies for different user types. For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances, simulating homogeneous connections, and autonomous behavior decisions. For ordinary users, we construct an inequality-oriented behavior decision-making module, effectively addressing unequal social interactions and capturing the patterns of relationship adjustments driven by multi-dimensional factors. Experimental results demonstrate that DynamiX exhibits marked improvements in attitude evolution simulation and collective behavior analysis compared to static networks. Besides, DynamiX opens a new theoretical perspective on follower growth prediction, providing empirical evidence for opinion leaders cultivation.

Jinyu Cai, Yusei Ishimizu, Mingyue Zhang et al.

Social media platforms frequently impose restrictive policies to moderate user content, prompting the emergence of creative evasion language strategies. This paper presents a multi-agent framework based on Large Language Models (LLMs) to simulate the iterative evolution of language strategies under regulatory constraints. In this framework, participant agents, as social media users, continuously evolve their language expression, while supervisory agents emulate platform-level regulation by assessing policy violations. To achieve a more faithful simulation, we employ a dual design of language strategies (constraint and expression) to differentiate conflicting goals and utilize an LLM-driven GA (Genetic Algorithm) for the selection, mutation, and crossover of language strategies. The framework is evaluated using two distinct scenarios: an abstract password game and a realistic simulated illegal pet trade scenario. Experimental results demonstrate that as the number of dialogue rounds increases, both the number of uninterrupted dialogue turns and the accuracy of information transmission improve significantly. Furthermore, a user study with 40 participants validates the real-world relevance of the generated dialogues and strategies. Moreover, ablation studies validate the importance of the GA, emphasizing its contribution to long-term adaptability and improved overall results.

Valeria Widler, Barbara Kaminska, Andre C. R. Martins et al.

The increasing polarization in democratic societies is an emergent outcome of political opinion dynamics. Yet, the fundamental mechanisms behind the formation of political opinions, from individual beliefs to collective consensus, remain unknown. Understanding that a causal mechanism must account for both bottom-up and top-down influences, we conceptualize political opinion dynamics as hierarchical coarse-graining, where microscale opinions integrate into a macro-scale state variable. Using the CODA (Continuous Opinions Discrete Actions) model, we simulate Bayesian opinion updating, social identity-based information integration, and migration between social identity groups to represent higher-level connectivity. This results in coarse-graining across micro, meso, and macro levels. Our findings show that higher-level connectivity shapes information integration, yielding three regimes: independent (disconnected, local convergence), parallel (fast, global convergence), and iterative (slow, stepwise convergence). In the iterative regime, low connectivity fosters transient diversity, indicating an informed consensus. In all regimes, time-scale separation leads to downward causation, where agents converge on the aggregate majority choice, driving consensus. Critically, any degree of coherent higher-level information integration can overcome misalignment via global downward causation. The results highlight how emergent properties of the causal mechanism, such as downward causation, are essential for consensus and may inform more precise investigations into polarized political discourse.

Mahmoud Fawzi, Björn Ross, Walid Magdy

Misinformation is a growing concern in a decade involving critical global events. While social media regulation is mainly dedicated towards the detection and prevention of fake news and political misinformation, there is limited research about religious misinformation which has only been addressed through qualitative approaches. In this work, we study the spread of fabricated quotes (Hadith) that are claimed to belong to Prophet Muhammad (the prophet of Islam) as a case study demonstrating one of the most common religious misinformation forms on Arabic social media. We attempt through quantitative methods to understand the characteristics of social media users who interact with fabricated Hadith. We spotted users who frequently circulate fabricated Hadith and others who frequently debunk it to understand the main differences between the two groups. We used Logistic Regression to automatically predict their behaviors and analyzed its weights to gain insights about the characteristics and interests of each group. We find that both fabricated Hadith circulators and debunkers have generally a lot of ties to religious accounts. However, circulators are identified by many accounts that follow the Shia branch of Islam, Sunni Islamic public figures from the gulf countries, and many Sunni non-professional pages posting Islamic content. On the other hand, debunkers are identified by following academic Islamic scholars from multiple countries and by having more intellectual non-religious interests like charity, politics, and activism.

H. Whitehouse, Jonathan A Lanman

Ziyi Yin, Guowei Huang, Rui Zhao et al.

Abstract Crowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project funding. However, current studies still lack investigations into the impact of information disclosure on green crowdfunding performance. This research aims to fill this knowledge gap by exploring eight information disclosure-relevant factors in green crowdfunding performance. Applying machine learning techniques (e.g., Natural Language Processing and Computer Vision) and logistic regression, this study investigates 720 green crowdfunding campaigns on GoFundMe and empirically finds that the duration, length of campaign introductions, and length of the title influence fundraising outcomes. However, no evidence supports the impact of goal size, emotion of campaign introduction, or image content on funding success. This study clarifies the information disclosure-related data that green crowdfunding campaigns should consider and provides founders with a constructive guide to smoothly raise money for a green crowdfunding campaign. This study also contributes to data processing methods by providing future studies with an approach for transferring unstructured data to structured data.

Afsha Fatima Qadri, Sibhghatulla Shaikh, Ye Chan Hwang et al.

Glycyrrhiza uralensis is a traditional herbal medicine with significant bioactivity. This study investigated the effect of G. uralensis crude water extract (GU-CWE) on nitric oxide synthase 2 (NOS2) expression during myogenesis. GU-CWE treatment increased myoblast differentiation by downregulating NOS2 and upregulating myogenic regulatory factors (MYOD, MYOG, and MYH). Notably, this effect was supported by an observed decrease in NOS2 expression in the gastrocnemius tissues of mice treated with GU-CWE. In addition, GU-CWE treatment and NOS2 knockdown were associated with reductions in reactive oxygen species levels. We further elucidate the role of the NOS2 gene in myoblast differentiation, demonstrating that its role was expression dependent, being beneficial at low expression but detrimental at high expression. High NOS2 gene expression induced oxidative stress, whereas its low expression impaired myotube formation. These findings highlight that the modulation of NOS2 expression by G. uralensis can potentially be use for managing muscle wasting disorders.

Ismail Hossain, Sai Puppala, Md Jahangir Alam et al.

User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using reaction emojis, reflecting their level of interest on social media such as Facebook, Twitter, and Reddit. Our objective is to analyze user history over time, including their posts and engagement on various topics. Additionally, we take into account the user's profile, seeking connections between their activities and social media platforms. By integrating user history, engagement, and persona, we aim to assess recommendation scores based on relevant item sharing by Hit Rate (HR) and the quality of the ranking system by Normalized Discounted Cumulative Gain (NDCG), where we achieve the highest for NeuMF 0.80 and 0.6 respectively. Our hybrid approach solves the cold-start problem when there is a new user, for new items cold-start problem will never occur, as we consider the post category values. To improve the performance of the model during cold-start we introduce collaborative filtering by looking for similar users and ranking the users based on the highest similarity scores.

Tunazzina Islam, Dan Goldwasser

Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "public sentiment towards vaccination", "political discourse surrounding climate policies," etc. In this paper, we introduce a novel approach to uncovering latent themes in social media messaging. Recognizing the limitations of the traditional topic-level analysis, which tends to capture only overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Traditional theme discovery methods typically involve manual processes and a human-in-the-loop approach. While valuable, these methods face challenges in scalability, consistency, and resource intensity in terms of time and cost. To address these challenges, we propose a machine-in-the-loop approach that leverages the advanced capabilities of Large Language Models (LLMs). To demonstrate our approach, we apply our framework to contentious topics, such as climate debate and vaccine debate. We use two publicly available datasets: (1) the climate campaigns dataset of 21k Facebook ads and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads. Our quantitative and qualitative analysis shows that our methodology yields more accurate and interpretable results compared to the baselines. Our results not only demonstrate the effectiveness of our approach in uncovering latent themes but also illuminate how these themes are tailored for demographic targeting in social media contexts. Additionally, our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.

Sai Puppala, Ismail Hossain, Md Jahangir Alam et al.

Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federated learning for privacy and security. Four client entities receive a base GPT-2 model and locally collected social media data, with federated aggregation ensuring up-to-date model maintenance. Subsequent modules focus on categorizing user posts, computing user persona scores, and identifying relevant posts from friends' lists. A quantifying social engagement approach, coupled with matrix factorization techniques, facilitates personalized content suggestions in real-time. An adaptive feedback loop and readability score algorithm also enhance the quality and relevance of content presented to users. Our system offers a comprehensive solution to content filtering and recommendation, fostering a tailored and engaging social media experience while safeguarding user privacy.

Angelien Meggersee, Sevias Guvuriro

Small mining towns are often single-industry towns that turn to ghost towns or face negative socio-economic impacts upon mine closure. This study qualitatively explores the roles that mining companies and other key stakeholders (should) play in the development of local economies of the small mining communities to bring about economic sustainability, employing a constant comparative analysis. A small mining town in South Africa is the case study. Legislative and policy frameworks were scrutinized for their effectiveness in promoting economic sustainability. In-depth interviews with key stakeholders were carried out. Key factors limiting the effective implementation of developmental strategies were also explored. The study finds that weak community involvement, lack of trust, poor collaboration, poor municipal capacity, and legislation and policy flaws impact economic sustainability. Sustainable local economic development efforts are reported though. However, such efforts are insufficient because of the legislation and policy frameworks that are promoting short-term growth. Also, the town’s overdependence on the mining company, local government not optimally fulfilling their roles and responsibilities, and minimal community members’ participation on local economic development are other hindrances. However, the fact that the mining company and local municipality do acknowledge the shortcomings in their efforts towards promoting economic sustainability is a promise in minimizing the socio-economic effects of mine closures.

Chengdong Yu, Jiawei Xu, Siyi Xu et al.

Background: Previous studies have discovered an association between dietary factors and breast cancer. However, few studies have used Mendelian randomization (MR) to assess the potential causal relationship between dietary factors and breast cancer. Methods: The exposure datasets for fresh fruit intake, dried fruit intake, salad/raw vegetable intake, cooked vegetable intake, oily fish intake, non-oily fish intake, cheese intake, and bread intake were obtained from the UK Biobank. The outcome dataset was extracted from the Breast Cancer Association Consortium (BCAC). We used the inverse variance weighted (IVW) method as the primary approach for the two-sample MR analysis. To ensure the accuracy of the results, we conducted heterogeneity and horizontal pleiotropy analyses. Additionally, multivariable MR analysis was conducted to ensure the stability of the results. Results: Dried fruit intake was found to be a protective factor for overall breast cancer (outliers excluded: OR: 0.549; 95 % CI: 0.429–0.702; p = 1.75 × 10−6). Subtype analyses showed that dried fruit intake was inversely associated with both estrogen receptor-positive (ER+) breast cancer (outliers excluded: OR: 0.669; 95 % CI: 0.512–0.875; p = 0.003) and ER-negative (ER−) breast cancer (OR: 0.559; 95 % CI: 0.379–0.827; p = 0.004), while fresh fruit intake was inversely associated with ER− breast cancer (excluded outliers: OR: 0.510; 95 % CI: 0.308–0.846; p = 0.009). No significant causal relationship was found between other dietary intakes and breast cancer. After adjusting for the effects of possible confounders, the causal relationships found by the two-sample MR analysis remained. Conclusion: Our study provides evidence that dried fruit intake may reduce the risk of both ER+ and ER− breast cancer, and fresh fruit intake may reduce the risk of ER− breast cancer. Other factors included in this study were not linked to breast cancer.

Hossein B. Jond, Aykut Yıldız

In a social network, individuals express their opinions on several interdependent topics, and therefore the evolution of their opinions on these topics is also mutually dependent. In this work, we propose a differential game model for the multi-dimensional opinion formation of a social network whose population of agents interacts according to a communication graph. Each individual's opinion evolves according to an aggregation of disagreements between the agent's opinions and its graph neighbors on multiple interdependent topics exposed to an unknown extraneous disturbance. For a social network with strategist agents the opinions evolve over time with respect to the minimization of a quadratic cost function that solely represents each individual's motives against the disturbance. We find the unique Nash/worst-case equilibrium solution for the proposed differential game model of coupled multi-dimensional opinions under an open-loop information structure. Moreover, we propose a distributed implementation of the Nash/worst-case equilibrium solution. We examine the non-distributed and proposed distributed open-loop Nash/worst-case strategies on a small social network with strategist agents in a two-dimensional opinion space. Then we compare the opinions evolved based on the Nash/worst-case strategy with the opinions corresponding to social optimality actions for non-strategist agents.

D. Moore

R. Duschl, Richard E. Grandy

G. Demiris, B. Hensel

Suzannah Evans Comfort, Young Eun Park

ABSTRACT The field of environmental communication has reached several milestones since the 1990s, particularly the establishment of environmental communication-related divisions at professional associations and the founding of the journal Environmental Communication in 2007. This systematic review characterizes the peer-reviewed literature on environmental communication to date, examining methods, geography, top-cited articles, and analyzing keyword and titles. Drawing on the Web of Science Core Collection, which archives the Social Sciences Citation Index and the Arts & Humanities Citation Index, the review finds that attention to environmental communication has exploded in recent years and that the field is methodologically open-minded. Scholars have shifted focus from general environmental risk to specifically climate change in the last decade. Implications for the field are discussed.