Our fight against false information is spearheaded by fact-checkers. They investigate the veracity of claims and document their findings as fact-checking reports. With the rapid increase in the amount of false information circulating online, the use of automation in fact-checking processes aims to strengthen this ecosystem by enhancing scalability. Datasets containing fact-checked claims play a key role in developing such automated solutions. However, to the best of our knowledge, there is no fact-checking dataset at the ecosystem level, covering claims from a sufficiently long period of time and sourced from a wide range of actors reflecting the entire ecosystem that admittedly follows widely-accepted codes and principles of fact-checking.

Abstract In the vein of recent work that provides non-normal modal interpretations of various topological operators, this paper proposes a modal logic for a spatial isolation operator. Focusing initially on neighbourhood systems, we prove several characterization results, demonstrating the adequacy of the interpretation and highlighting certain semantic insensitivities that result from the relative expressive weakness of the isolation operator. We then transition to the topological setting, proving a topological characterization result.

The recent digital transformation of higher education underscores the crucial role of STEM disciplines in addressing real-world challenges, emphasizing the urgent need for problem-solving skills, creativity, and diversity within the STEM student community. Despite growing awareness of the gender disparity in STEM professions, women remain underrepresented in these fields. This paper conducts a comprehensive study delving into the determinants influencing the decision to pursue a STEM degree and the challenges faced during STEM education. Through survey data analysis, the research explores actionable mechanisms to enhance the presence of women in STEM subjects and support their career progression. The study not only identifies obstacles but also formulates a set of recommendations to establish a robust support system for women in STEM. These recommendations aim to foster their academic retention, facilitate continuous professional development, and contribute to narrowing the gender gap in STEM occupations. By addressing the root causes and proposing concrete solutions, this research seeks to contribute to a more inclusive and equitable STEM landscape, fostering an environment where women can thrive and make significant contributions to the ever-evolving field of science and technology. Furthermore, this research serves as a call to action for institutions, policymakers, and stakeholders to collectively champion initiatives that empower and propel women to excel in STEM fields.

In this paper, we introduce public announcement logic in different geometric frameworks. First, we consider topological models, and then extend our discussion to a more expressive model, namely, subset space models. Furthermore, we prove the completeness of public announcement logic in those frameworks. After that, we apply our results to different issues: announcement stabilization, backward induction and persistence.