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Mental health care needs of caregivers of people with Alzheimer’s disease from online forum analysis
Informal caregivers of people with Alzheimer’s disease and related dementias (ADRD) are at risk of poor mental health. This study aimed to investigate the feasibility and validity of studying caregivers’ mental stressors using online caregiving forum data (March 2018–February 2022) and natural language processing and machine learning (NLP/ML). NLP/ML topic modeling generated eight prominent topics, which we compared with qualitatively defined themes and the existing caregiving framework to assess validity. Among a total of 60,182 posts, 5848 were mental distress-related; for the ADRD patients (symptoms, medication, relocation, care duty share, diagnosis, conversation strategy) and the caregivers (caregiving burden and support). While we observed novel topics from NLP/ML-defined topics, mostly those were aligned with the existing framework. For feasibility assessment, qualitative title screening was done. The findings shed new light on the potential of NLP/ML text analysis of the online forum for informal caregivers to prepare tailored support for this vulnerable population.
Bitcoin research with a transaction graph dataset
Bitcoin, introduced in 2008 by Satoshi Nakamoto, revolutionized the digital economy by enabling decentralized value storage and transfer, eliminating the need for a central authority. This paper presents a large-scale, temporally annotated graph dataset representing Bitcoin transactions, designed to advance research in blockchain analytics and beyond. The dataset comprises 252 million nodes and 785 million edges, with each node and edge timestamped for temporal analysis. To support supervised learning, we provide two labeled subsets: (i) 34,000 nodes annotated with entity types, and (ii) 100,000 Bitcoin addresses labeled with entity names and types. This dataset is the largest publicly available resource of its kind, addressing the limitations of existing datasets and enabling advanced exploration of Bitcoin’s transaction network. We establish baseline performance using graph neural network models for node classification tasks. Furthermore, we highlight additional use cases, including fraud detection, network analysis, and temporal graph learning, demonstrating its broader applicability beyond Bitcoin. We release the complete dataset, along with source code and benchmarks, to the public.
Space Analogs and Behavioral Health Performance Research review and recommendations checklist from ESA Topical Team
Space analog research has increased over the last few years with new analogs appearing every year. Research in this field is very important for future real mission planning, selection and training of astronauts. Analog environments offer specific characteristics that resemble to some extent the environment of a real space mission. These analog environments are especially interesting from the psychological point of view since they allow the investigation of mental and social variables in very similar conditions to those occurring during real space missions. Analog missions also represent an opportunity to test operational work and obtain information on which combination of processes and team dynamics are most optimal for completing specific aspects of the mission. A group of experts from a European Space Agency (ESA) funded topical team reviews the current situation of topic, potentialities, gaps, and recommendations for appropriate research. This review covers the different domains in space analog research including classification, main areas of behavioral health performance research in these environments and operational aspects. We also include at the end, a section with a list or tool of recommendations in the form of a checklist for the scientific community interested in doing research in this field. This checklist can be useful to maintain optimal standards of methodological and scientific quality, in addition to identifying topics and areas of special interest.
Fossil fuel-exporting countries have the responsibility and resources to accelerate CCUS adoption
Overcoming sociotechnical barriers CCUS adoption faces multiple interlinked challenges across technological, economic, regulatory and social dimensions. Understanding this multi-faceted challenge through a sociotechnical systems lens,…
Event triggers and opinion leaders shape climate change discourse on Weibo
Understanding how real-world events and opinion leaders shape climate change discussions is vital for improving communication and policy formulation to meet global carbon mitigation goals. This study analyzed 5.3 million original posts from Weibo (2012–2022), China’s largest social media platform, to examine climate change discourse. We found five event types triggering 48 discussion peaks, including online activities, international conferences, extreme weather, domestic policies, and international news. Posts generally conveyed positive attitudes, though sentiment decreased during haze pollution and the COVID-19 pandemic. Network analysis revealed seven opinion leader groups with distinct strategies: official media and institutions emphasized political will, global initiatives, and socio-economic implications, while universities and grassroots individuals focused on scientific reality and personal actions. Celebrities and unofficial accounts often highlighted geopolitical topics, especially China-US relations. We suggest reducing fragmented echo chambers and fostering personal connections through digital media platforms to enhance public awareness.
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