Should we tweet this? Generative response modeling for predicting reception of public health messaging on Twitter

The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19. It could be valuable for high-impact organizations such as the US Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) to understand how these perceptions impact reception of messaging on health policy recommendations. We collect two datasets of public health messages and their responses from Twitter relating to COVID-19 and Vaccines, and introduce a predictive method which can be used to explore the potential reception of such messages. Specifically, we harness a generative model (GPT-2) to directly predict probable future responses and demonstrate how it can be used to optimize expected reception of important health guidance. Finally, we introduce a novel evaluation scheme with extensive statistical testing which allows us to conclude that our models capture the semantics and sentiment found in actual public health responses.Comment: Accepted at ACM WebSci 202

A Mathematics Pipeline to Student Success in Data Analytics through Course-Based Undergraduate Research

This paper reports on Data Analytics Research (DAR), a course-based undergraduate research experience (CURE) in which undergraduate students conduct data analysis research on open real- world problems for industry, university, and community clients. We describe how DAR, offered by the Mathematical Sciences Department at Rensselaer Polytechnic Institute (RPI), is an essential part of an early low-barrier pipeline into data analytics studies and careers for diverse students. Students first take a foundational course, typically Introduction to Data Mathematics, that teaches linear algebra, data analytics, and R programming simultaneously using a project-based learning (PBL) approach. Then in DAR, students work in teams on open applied data analytics research problems provided by the clients. We describe the DAR organization which is inspired in part by agile software development practices. Students meet for coaching sessions with instructors multiple times a week and present to clients frequently. In a fully remote format during the pandemic, the students continued to be highly successful and engaged in COVID-19 research producing significant results as indicated by deployed online applications, refereed papers, and conference presentations. Formal evaluation shows that the pipeline of the single on-ramp course followed by DAR addressing real-world problems with societal benefits is highly effective at developing students\u27 data analytics skills, advancing creative problem solvers who can work both independently and in teams, and attracting students to further studies and careers in data science

Survey of Stormwater BMP Maintenance Practices

Many stormwater management manuals and guidance documents have stated the importance and estimated frequency of maintenance for stormwater best management practices (BMPs), but few have documented the actual frequency and intensity of maintenance required to maintain a desired level of performance and efficiency. Increased attention to mass balance, numerical goals, total maximum daily loads (TMDLs), and non-degradation requirements has created the need for more emphasis on BMP maintenance in order to meet permitting and reporting requirements. The purpose of this paper is to advance short and long-term maintenance considerations so as to develop more realistic maintenance plans. To do so, we conducted a national literature search for maintenance costs and developed, distributed, analyzed the results of a detailed municipal public works survey. The specific goals of the survey were to identify and inventory stormwater BMP O&M efforts and costs. Survey questionnaires were sent to 106 cities with 28 responses received. The survey related to the following topics: number of BMPs in the city, frequency of BMP inspections, average staff-hours spent per routine inspection/maintenance, complexity of BMP maintenance, most frequent causes of performance deterioration within BMPs, and cost of non-routine maintenance activities. The results of the survey revealed that most (89%) cities perform routine maintenance once per year or less. Staff-hours per year ranged from one to four hours for most stormwater BMPs and but were significantly more for rain gardens (one to sixteen hours per year) and wetlands (one to nine hours per year). The most common causes of performance deterioration were sediment buildup and litter/debris for most stormwater BMPs. Respondents indicated that the removal of accumulated sediment incurred the largest cost of all BMP maintenance activities