Search Results (149)

The recent identification of early-onset mutational signatures with geographic variations by Diaz-Gay et al. is a significant finding, since early-onset colorectal cancer has emerged as an alarming public health challenge in the past two decades, and the pathomechanism remains unclear. Environmental risk factors, including lifestyle and diet, are highly suspected. The identification of colibactin from Escherichia coli as a potential pathogenic source is a major step forward in addressing this public health challenge. Therefore, the following opinion manuscript aims to outline the likely onset of the pathomechanism and the critical role of acquired Piezo2 channelopathy in early-onset colorectal cancer, which skews proton availability and proton motive force regulation toward E. coli within the microbiota–host symbiotic relationship. In addition, the colibactin produced by the pks island of E. coli induces host DNA damage, which likely interacts at the level of Wnt signaling with Piezo2 channelopathy-induced pathological remodeling. This transcriptional dysregulation eventually leads to tumorigenesis of colorectal cancer. Mechanotransduction converts external physical cues to inner chemical and biological ones. Correspondingly, the proposed quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling, initiated by Piezo2, seems to be the principal and essential underlying novel oscillatory signaling that could be lost in colorectal cancer onset. Hence, Piezo2 channelopathy not only contributes to cancer initiation and impaired circadian regulation, including the proposed hippocampal ultradian clock, but also to proliferation and metastasis. Full article

Background and aims: The Last Aid Course (LAC) has been established to enhance the discussion about dying, death and grief and to raise the public’s awareness of palliative care. The aim of this study was to explore the views and experiences of German Last Aid Course instructors with the LAC as means for Public Palliative Care Education (PPCE), including their opinion about the course content and format and practical aspects of teaching in different settings. Methods: A longitudinal mixed-methods approach was used to explore the views and experiences of the Last Aid Course instructors over a period of five years. Social space orientation was used as the framework for the data analysis. Results: The LAC participants felt empowered after the LACs. Continuing development was a characteristic of the LAC project. The positive effects of the LACs included empowerment and positive interactions between the instructors and participants. In addition, the LACs had a positive impact on all five principles of social space orientation. Conclusions: LACs can contribute to raising public awareness about dying, death, grief and palliative care and empower people to participate in caring for those who are serious ill, dying and grieving. Full article

The evolution of public opinion on social media affects societal security and stability. To effectively control the societal impact of public opinion evolution, it is essential to study its underlying mechanisms. Public opinion evolution on social media primarily involves two processes: information dissemination and opinion interaction. However, existing studies overlook the bidirectional coupling relationship between these two processes, with limitations such as weak coupling and insufficient consideration of individual heterogeneity. To address this, we propose the SIS-FJ model with a bidirectional coupling mechanism, which combines the strengths of the SIS (Susceptible–Infected–Susceptible) model in information dissemination and the FJ (Friedkin–Johnsen) model in opinion interaction. Specifically, the SIS model is used to describe information dissemination, while the FJ model is used to describe opinion interaction. In the computation of infection and recovery rates of the SIS model, we introduce the opinion differences between individuals and their observable neighbors from the FJ model. In the computation of opinion values in the FJ model, we introduce the node states from the SIS model, thus achieving bidirectional coupling between the two models. Moreover, the model considers individual heterogeneity from multiple aspects, including infection rate, recovery rate, and individual susceptibility. Through simulation experiments, we investigate the effects of initial opinion distribution, individual susceptibility, and network structure on public opinion evolution. Interestingly, neither initial opinion distribution, individual susceptibility, nor network structure exerts a significant influence on the proportion of disseminating and non-disseminating individuals at termination. Furthermore, we optimize the model by adjusting the functions for infection and recovery rates. Full article

Climate change presents serious challenges for infrastructure, regional planning, and public awareness. However, effectively understanding and analyzing large-scale climate discussions remains difficult. Traditional methods often struggle to extract meaningful insights from unstructured data sources, such as social media discourse, making it harder to track climate-related concerns and emerging trends. To address this gap, this study applies Natural Language Processing (NLP) techniques to analyze large volumes of climate-related data. By employing supervised and weak supervision methods, climate data are efficiently labeled to enable targeted analysis of regional- and infrastructure-specific climate impacts. Furthermore, BERT-based Named Entity Recognition (NER) is utilized to identify key climate-related terms, while sentiment analysis of platforms like Twitter provides valuable insights into trends in public opinion. AI-driven visualization tools, including predictive modeling and interactive mapping, are also integrated to enhance the accessibility and usability of the analyzed data. The research findings reveal significant patterns in climate-related discussions, supporting policymakers and planners in making more informed decisions. By combining AI-powered analytics with advanced visualization, the study enhances climate impact assessment and promotes the development of sustainable, resilient infrastructure. Overall, the results demonstrate the strong potential of AI-driven climate analysis to inform policy strategies and raise public awareness. Full article

The rapid development of online media has significantly facilitated the public’s information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has primarily focused on larger datasets and more complex computational models but has rarely examined the underlying mechanisms of conflict, particularly the psychological motivations behind them. In this paper, we propose a lightweight and language-independent method for controversy detection by introducing two novel psychological features: ascending gradient (AG) and tier ascending gradient (TAG). These features capture psychological signals in user interactions—specifically, the patterns where controversial comments generate disproportionate replies or replies outperform parent comments in likes. We develop these features based on the theory of the backfire effect in ideological conflict and demonstrate their consistent effectiveness across models and platforms. Compared with structural, interaction, and text-based features, AG and TAG show higher importance scores and better generalizability. Extensive experiments on Chinese and English platforms (Reddit, Toutiao, and Sina) confirm the robustness of our features across languages and algorithms. Moreover, the features exhibit strong performance even when applied to early-stage data or limited “one-page” scenarios, supporting their utility for early controversy detection. Our work highlights a new psychological perspective on conflict behavior in online discussions and bridges behavioral patterns and computational modeling. Full article

With the rapid development of the Internet, the speed and scope of sudden public events disseminating in cyberspace have grown significantly. Current methods of quantifying public opinion heat often neglect emotion-driven factors and user interaction behaviors, making it difficult to accurately capture fluctuations during dissemination. To address these issues, first, this study addressed the complexity of interaction behaviors by introducing an approach that employs the information gain ratio as a weighting indicator to measure the “interaction heat” contributed by different interaction attributes during event evolution. Second, this study built on SnowNLP and expanded textual features to conduct in-depth sentiment mining of large-scale opinion texts, defining the variance of netizens’ emotional tendencies as an indicator of emotional fluctuations, thereby capturing “emotional heat”. We then integrated interactive behavior and emotional conflict assessment to achieve comprehensive heat index to quantification and dynamic evolution analysis of online public opinion heat. Subsequently, we used Hodrick–Prescott filter to separate long-term trends and short-term fluctuations, extract six key quantitative features (number of peaks, time of first peak, maximum amplitude, decay time, peak emotional conflict, and overall duration), and applied K-means clustering algorithm (K-means) to classify events into three propagation patterns, which are extreme burst, normal burst, and long-tail. Finally, this study conducted ablation experiments on critical external intervention nodes to quantify the distinct contribution of each intervention to the propagation trend by observing changes in the model’s goodness-of-fit ($R^2$) after removing different interventions. Through an empirical analysis of six representative public opinion events from 2024, this study verified the effectiveness of the proposed framework and uncovered critical characteristics of opinion dissemination, including explosiveness versus persistence, multi-round dissemination with recurring emotional fluctuations, and the interplay of multiple driving factors. Full article

Background: The Museum for Anatomy and Pathology at the Radboud University (The Netherlands) has created a permanent teratological exhibition, which is enhanced with augmented reality (AR) modalities. This exhibition serves various (post)graduate educational purposes and is open to the general public. However, data on visitors’ views and experiences regarding the teratological collection and AR models are currently lacking. Methods: To address this, a qualitative study was conducted to explore visitors’ opinions and experiences. One-on-one in-depth interviews were conducted using a predefined topic list, with audio recordings transcribed verbatim. Thematic analysis was applied to the twenty-six interview transcripts. Results: The findings indicate that publicly displaying teratological specimens alongside AR modalities is valued and positively received by both (bio)medical students and laypeople alike. AR enhances understanding of dysmorphology and provides a more interactive and engaging learning experience for complex topics. Conclusion: The use of AR within a teratological exposition holds tremendous educational potential and improves public awareness and acceptance of developmental anomalies. Moreover, it provides a unique opportunity to reflect on both historical and contemporary bioethical issues. Full article

With frequent interactions between social media platforms, the dissemination of information and the interaction of opinions on the internet have become increasingly complex and diverse. This increase in information complexity not only affects the formation of public opinion but may also exacerbate the spread of diseases. Based on multilayer complex networks and combined with the Deffuant-I model, this paper explores the dual impact of information complexity and individual characteristics on both information and disease propagation. Through systematic simulation experiments, this paper analyzes the mechanisms of information complexity, individual compromise, and cognitive ability in the evolution of propagation. This study shows that the interactive effects of individual characteristics and information complexity have a significant impact on disease spread. This research not only provides a new theoretical perspective for understanding complex information dissemination but also offers valuable insights for public policymakers in promoting social harmony and addressing public health emergencies. Full article

Faced with mounting pressure to achieve high-quality green transformation, manufacturing enterprises are increasingly scrutinized for greenwashing behaviors. This study develops a novel hybrid modeling framework that combines evolutionary game theory with the SEIR epidemic model to investigate the dynamic interactions between environmental regulation, media pressure, and green innovation behavior. The model captures how strategic decisions among boundedly rational actors evolve over time under dual external pressures. Simulation results show that stronger environmental regulatory intensity accelerates the adoption of substantive green innovation and concurrently reduces the media pressure associated with greenwashing. Moreover, while social media disclosure has a limited impact during the early stages of greenwashing information diffusion, its influence becomes significantly amplified once a critical dissemination threshold is surpassed, rapidly transforming latent information into widespread public concern. This amplification triggers significant public opinion pressure, which, in turn, incentivizes local governments to enforce stricter environmental policies. The findings reveal a synergistic governance mechanism where environmental regulation and media scrutiny jointly curb greenwashing and foster genuine corporate sustainability. Full article

Understanding the dynamics of public opinion formation in digital environments is crucial for managing technological communications effectively. This study investigates stance interactions and opinion reversal phenomena in technical discourse through analysis of the Manus AI controversy that generated approximately 36,932 social media interactions during March 2025. Employing an integrated methodology combining Large Language Model (LLM)-enhanced stance detection with agent-based modeling (ABM), we reveal distinctive patterns challenging traditional public opinion theories. Our cross-correlation analysis identifies significant lagged interaction effects between skeptical and supportive stances, demonstrating how critical expressions trigger amplified counter-responses rather than inducing silence. Unlike prior conceptualizations of counter-silencing that emphasize ideological resistance or echo chambers, our notion of the “counter-spiral of silence” specifically highlights lagged emotional responses and reactive amplification triggered by minority expressions in digital technical discourse. We delineate its boundary conditions as arising under high emotional salience, asymmetrical expertise, and platform structures that enable real-time feedback. The agent-based simulation reproduces empirical patterns, revealing how emotional contagion and network clustering mechanisms generate “counter-spiral of silence” phenomena where challenges to dominant positions ultimately strengthen rather than weaken those positions. These findings illuminate how cognitive asymmetries between public expectations and industry realities create distinctive discourse patterns in technical contexts, offering insights for managing technology communication and predicting public response trajectories in rapidly evolving digital environments. Full article

Green innovation, which promotes the coordinated development of the economy and ecology, serves as a critical means to achieve enterprises’ green transformation. Against the backdrop of the Internet era, retail investors, as an important supervisory group for enterprises, can generate online public opinion through interactive exchanges on social media platforms. This raises the question: Can such public opinion rooted in social media influence enterprises’ green innovation behaviors? To address this, this study uses data from Chinese A-share listed enterprises in heavily polluting industries on the Shanghai and Shenzhen Stock Exchanges from 2008–2021, comprising a total sample size of 8755, and employs ordinary least squares (OLS) regression models to empirically examine the relationship between retail investors’ social media interactions and enterprise green innovation. The findings reveal that interactive discussions by retail investors on social media significantly enhance enterprises’ green innovation levels. Mechanism tests show that social media interactions among these investors strengthen enterprises’ environmental awareness and alleviate their financing constraints, thereby promoting green innovation. Moderation effect tests indicate that the quality of social media information interaction and public opinion sentiment positively moderate the relationship between retail’s social media interactions and enterprise green innovation. Heterogeneity tests further show that the positive effect of retail’s social media interactions on enterprise green innovation is more pronounced in regions with stronger environmental information regulation and stronger investor protection. The conclusions of this study not only enrich research on the relationship between retail investors’ social media supervision and enterprises’ behavioral decision-making but also extend the literature on the influencing factors of enterprise green innovation from the perspective of public governance. These findings hold important implications for enterprises’ green transformation practices under the “double carbon” goals and provide valuable insights for corporate governance in the era of the digital economy. Full article

Event extraction aims to identify and structure event information from unstructured text, playing a critical role in real-world applications such as news analysis, public opinion discovery, and intelligence gathering. Traditional approaches, however, struggle with event co-occurrence and long-distance dependencies. To address these challenges, we introduce the Semantic-aware Prompt-based Argument Extractor (SPARE) model, which integrates entity extraction, heterogeneous graph construction, event type detection, and argument filling. By constructing a document–sentence–entity heterogeneous graph and employing graph convolutional networks (GCNs), the model effectively captures global semantic associations and interactions between cross-sentence triggers and arguments. Additionally, a position-aware semantic role (SRL) attention mechanism is proposed to enhance the association between semantic and positional information, improving argument extraction accuracy in the context of event co-occurrence. The experimental outcomes on the Richly Annotated Multilingual Schema-guided Event Structure (RAMS) and WikiEvents datasets display considerable F1 score improvements, which confirms the model’s effectiveness. Full article

This study employs evolutionary game theory to analyze the tripartite interaction among government regulators, media publishers, and self-media participants in emergency public opinion management. We establish an evolutionary game model incorporating strategic motivations and key influencing factors; then, we validate the model through systematic simulations. Key findings demonstrate the following: ① the system exhibits dual stable equilibria: regulated equilibrium and autonomous equilibrium. ② Sensitivity analysis identifies critical dynamics: ① self-media behavior is primarily driven by penalty avoidance ($g_3$) and losses ($w_2$); ② media participation hinges on revenue incentives ($m_2$) versus regulatory burdens ($k$); ③ government intervention efficacy diminishes on emergencies when resistance ($v_1$ + $v_3$) exceeds control benefits. The study reveals that effective governance requires the following: ① adaptive parameter tuning of punishment–reward mechanisms; ② dynamic coordination between information control and market incentives. This framework advances emergency management by quantifying how micro-level interactions shape macro-level opinion evolution, providing actionable insights for balancing stability and information freedom in digital governance. Full article

This research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This study addresses limitations in existing research, including inaccurate data collection and a lack of systematic analysis. By incorporating Jieba Chinese word segmentation technology, this study introduces an innovative data collection method based on topic similarity, significantly improving data accuracy. Additionally, this research establishes a comprehensive public opinion analysis framework that integrates user follower counts, geographical distribution, and interaction data. This framework facilitates the identification of sources of negative sentiment and the development of effective response strategies. As a case study, the dissemination patterns of EMR-related public opinion on Weibo are analyzed, focusing on group sentiment and social interaction. The proposed system achieves a 65.85% improvement in data collection accuracy, demonstrating its effectiveness. Furthermore, this study provides actionable recommendations for relevant departments and governments to monitor, analyze, and respond to EMR-related public opinion. By enhancing decision-making and protecting public interests, this study highlights the role of technology in improving social governance and substantial development. Full article

This study explores explicit and implicit knowledge in large-scale linguistic data and digital footprints from social networks. This research aims to develop and test algorithms for analyzing both explicit and implicit information in user-generated content and digital interactions. A dataset of social media discussions on avian influenza in Moscow (RF) was collected and analyzed (tokens: 1,316,387; engagement: 108,430; audience: 39,454,014), with data collection conducted from 1 March 2023, 00:00 to 31 May 2023, 23:59. This study employs Brand Analytics, TextAnalyst 2.32, ChatGPT o1, o1-mini, AutoMap, and Tableau as analytical tools. The findings highlight the advantages and limitations of explicit and implicit information analysis for social media data interpretation. Explicit knowledge analysis is more predictable and suitable for tasks requiring quantitative assessments or classification of explicit data, while implicit knowledge analysis complements it by enabling a deeper understanding of subtle emotional and contextual nuances, particularly relevant for public opinion research, social well-being assessment, and predictive analytics. While explicit knowledge analysis provides structured insights, it may overlook hidden biases, whereas implicit knowledge analysis reveals underlying issues but requires complex interpretation. The research results emphasize the importance of integrating various scientific paradigms and artificial intelligence technologies, particularly large language models (LLMs), in the analysis of social networks. Full article