In this study, an improved version of the Beluga whale optimization (BWO) algorithm, which is a meta-heuristic optimization algorithm recently presented in the literature, is developed to provide better solutions for the problems. The fitness-distance balance (FDB) selection method was applied in the search processes in the BWO algorithm, which was developed by modeling the swimming, preying and falling characteristics of beluga whales. CEC2020 benchmark functions were used to test the performance of the BWO algorithm and the algorithm named FDBBWO. The algorithms were tested on these test functions for 30, 50 and 100 dimensions. Friedman analysis was performed on the test results and the performance ranks of the algorithms were determined. In addition, Wilcoxon rank sum test was used to analyze whether there were significant differences in the results. As a result of the experimental study, it is observed that the BWO algorithm improves the early convergence problem that may arise due to the lack of diversity in the search process. In this way, the possibility of getting stuck at local optimum points is reduced. In addition, the developed algorithm is compared with 3 different algorithms that have been recently presented in the literature. According to the comparison results, FDBBWO has a superior performance compared to other meta-heuristic algorithms.

In this study, a novel hybridization approach, which is called CMASFS and is based on the covariance matrix adaptation evolution strategy (CMA-ES) and the stochastic fractal search (SFS) algorithms. To make the proposed algorithm dynamic, Gaussian walk equations involved in the diffusion process of SFS have been updated and the algorithm decide to use which the Gaussian walk equations. The effectiveness of the proposed algorithm is tested using CEC2017 benchmark functions having unimodal, multimodal, hybrid, and composition functions in 10, 30, 50, and 100 dimensions. The performance of the CMASFS algorithm is compared with 17 metaheuristic algorithms given in the literature over the CEC2017 benchmark functions. According to the results, it is seen that CMASFS is generally obtained better mean error values. Moreover, to show the superiority of the proposed algorithm, Friedman analysis and the Wilcoxon rank-sum test are applied to the test results of the algorithms. The results of the Wilcoxon signed-rank test show that the improvement with the CMASFS algorithm is statistically significant on the majority of the CEC2017. The results of Friedman test verify that the CMASFS is obtained the best rank compared to both the original SFS and other compared algorithms.

Green finance has become a key component of sustainability transitions by supporting the alignment of financial systems with environmental and climate-related objectives. As discussions on sustainable investments and climate finance increasingly take place in digital environments, analyzing large-scale user-generated content has become important for understanding online discussions and emerging discourse patterns. This study investigates green finance discourse in the digital public sphere using a multi-method Natural Language Processing (NLP) framework. A dataset of 23,452 posts collected from the platform X was analyzed using TF-IDF-based word frequency analysis, FinBERT-based sentiment analysis, Latent Dirichlet Allocation (LDA) topic modelling, and keyword co-occurrence network analysis. The results indicate that green finance discourse is dominated by positive (44.61%) and neutral (45.85%) sentiment, suggesting a generally favorable and institutionalized public perception. Topic modelling identified three dominant themes: climate finance and sustainability transition, digital finance and speculative investment narratives, energy investments and financial infrastructure. Network analysis revealed that sustainability, climate, energy, investment, and finance constitute the core conceptual structure of discourse. These findings demonstrate the effectiveness of NLP-based approaches for analyzing large-scale digital discussions and provide insights for policymakers, financial institutions, and organizations seeking to better understand online discussions surrounding green finance and support more effective sustainability communication strategies.

International and national statistics indicate that Turkey leads OECD countries in the prevalence of violence against women, alongside a rising trend in femicides. Recognizing that social media platforms do more than report events—they actively shape collective memory—this study reconceptualizes X (formerly Twitter) as a dynamic communication ecosystem. We analyze 30,613 original Turkish-language tweets posted between 7 November 2022 and 16 January 2023 using a mixed-methods pipeline: automated text mining for hashtag networks and engagement metrics, combined with a two-stage content and discourse analysis that preserves key platform affordances such as hashtags, mentions, emojis, and thread depth. Drawing on second-level agenda-setting theory, Bruns & Moe’s algorithmic amplification research, and Ferron & Massa’s digital memory framework, we identify three strategic communication phenomena: “Anger Flashpoints” (hashtag-driven outrage cascades), “Rights-Claiming Petitions” (mention-mediated appeals to institutions), and “Collective Resistance Debates” (thread-based deliberations). Our findings reveal that spikes in discourse align with rapid retweet cascades and trending-topic promotions; direct mentions function as crowd-sourced petitions for legal and policy change; and extended reply threads nurture sustained counter-narratives. These thematic clusters act as both emotional expressions and tactical communication tools, leveraging X’s algorithmic affordances to coordinate public outrage and embed discussions of femicide within social memory. By highlighting these communicative mechanisms, we demonstrate X’s dual role as both a mirror and driver of public sentiment, emphasizing its potential to influence policy debates and support advocacy against violence toward women.