This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

In the context of socio-technical systems, traditional engineering approaches are inadequate, calling for a fundamental change in perspective. A different approach encourages viewing socio-technical systems as complex living entities rather than through a simplistic lens, which enhances our understanding of their dynamics. However, these systems are designed to facilitate human activities, and the goal is not only to comprehend how they operate but also to guide their function. Currently, we lack the appropriate terminology. Hence, we introduce two principal concepts, simplexity and complixity, drawing inspiration from how nature conceals intricate mechanisms beneath straightforward, user-friendly interfaces.

Scientific disinformation has emerged as a critical challenge at the interface of science and society. This paper examines how false or misleading scientific content proliferates across both social media and traditional media and evaluates strategies to counteract its spread. We conducted a comprehensive literature review of research on scientific misinformation across disciplines and regions, with particular focus on climate change and public health as exemplars. Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. Traditional media, meanwhile, are not immune to spreading inaccuracies—journalistic practices such as sensationalism or “false balance” in reporting have at times distorted scientific facts, impacting public understanding. We review efforts to fight disinformation, including technological tools for detection, the application of inoculation theory and prebunking techniques, and collaborative approaches that bridge scientists and journalists. To empower individuals, we propose practical guidelines for critically evaluating scientific information sources and emphasize the importance of digital and scientific literacy. Finally, we discuss methods to quantify the prevalence and impact of scientific disinformation—ranging from social network analysis to surveys of public belief—and compare trends across regions and scientific domains. Our results underscore that combating scientific disinformation requires an interdisciplinary, multi-pronged approach, combining improvements in science communication, education, and policy. We conducted a scoping review of 85 open-access studies focused on climate-related misinformation and disinformation, selected through a systematic screening process based on PRISMA criteria. This approach was chosen to address the lack of comprehensive mappings that synthesize key themes and identify research gaps in this fast-growing field. The analysis classified the literature into 17 thematic clusters, highlighting key trends, gaps, and emerging challenges in the field. Our results reveal a strong dominance of studies centered on social media amplification, political denialism, and cognitive inoculation strategies, while underlining a lack of research on fact-checking mechanisms and non-Western contexts. We conclude with recommendations for strengthening the resilience of both the public and information ecosystems against the spread of false scientific claims.

This communication examines the interplay between linguistic mediation and knowledge conversion in cyber-sociotechnical systems (CSTSs) via the WAx framework, which outlines various work representations and eight key conversion activities. Grounded in enactivist principles, we argue that language is a dynamic mechanism that shapes, and is shaped by, human–machine interactions, enhancing system resilience and adaptability. By integrating the concepts of simplexity, complixity, and complexity compression, we illustrate how complex cognitive and operational processes can be selectively condensed into efficient outcomes. A case study of a chatbot-based customer support system demonstrates how the phases of socialization, introspection, externalization, combination, internalization, conceptualization, reification, and influence collaboratively drive the evolution of resilient CSTS designs. Our findings indicate that natural language serves as a bridging tool for effective sense-making, adaptive coordination, and continuous learning, offering novel insights into designing technologically advanced, socially grounded, and evolving sociotechnical systems.

Carbonate ramp systems present significant seismic interpretation challenges due to their pronounced facies heterogeneity, which frequently results in chaotic seismic outputs that obscure the underlying geological structures. The Porto Badisco Calcarenite in Salento, southern Italy, an Oligocene carbonate ramp, serves as the case study for this research, offering an analogue for understanding similar geological systems. By integrating fieldwork, laboratory analysis and MATLAB modelling, this study pioneers the use of detailed petrophysical data to construct innovative velocity models based on the velocity ranges of the different lithofacies analysed. These models distinctly illustrate the impact of facies heterogeneity on seismic velocities, providing fresh insights into acoustic impedance and variable propagation velocities across different facies constituting the carbonate ramp. Through advanced high-resolution synthetic seismic modelling conducted on carefully fine-tuned unmigrated stack sections, the research demonstrates how variations in petrophysical characteristics within measured ranges reflecting carbonate textures can dramatically alter seismic imaging. The innovative models, based on propagation velocity ranges, not only deepen the understanding of the seismic representation of lithofacies but also act as a potent tool for probing the subsurface architecture of complex carbonate systems, providing an interpretative key for the analysis of seismic images. This approach signifies a substantial advancement in seismic modelling that is aimed at refining interpretations and enhancing exploration strategies in carbonate ramp environments globally.