Browse Articles

The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.

The authors use MoralBERT, a BERT-based machine learning model, to analyze the prevalence and evolution of moral language in art critique journals from 1960 to 2015. They draw on Moral Foundations Theory to examine changes in five virtue and five vice foundations over time. They discover slight increases in degradation and care and a decrease in purity among otherwise stable patterns. The study is a novel application of computational moral analysis in a domain not typically associated with such tools. Although the moral signals were relatively faint, the study offers insights into shifting art world discourses and models an exciting method for future inquiry.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

Here the authors investigated the motivation of a short squeeze of GameStop stock where users of the internet forum Reddit drove a sudden increase in GameStop stock price during the start of 2021. They relied on both qualitative and quantitative analyses where they tracked activity on the r/WallStreetBets subreddit in relation to mentions of GameStop. With these methods they found that while initially the short squeeze was driven by financial motivations, later on emotional motivations became more important. They suggest that social phenomena can be dynamic and evolve necessitating mixed method approaches to study them.

The authors looked at beach nourishment (a way to combat erosion on coasts) and resulting grain size distribution. Their work is important to understand the dynamics of erosion and it's relation to wave action and the implications this has for efforts to mitigate coastal erosion.

This study analyzes over two decades of monitoring data (1999-2022) to investigate how legacy mining, reservoir water releases, and dredging activities influence toxic methylmercury (MeHg) levels in San Francisco Bay. The findings reveal a significant delayed correlation between river flow and San Francisco Bay MeHg, and counter to the authors' hypothesis, a strong association between increased MeHg concentrations in the bay and both total annual dredging volume and beneficial sediment reuse / upland sediment disposal.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.

This study examines how environmental conditions influence the abundance and ecological role of the sea cucumber Opheodesoma spectabilis in Kāneʻohe Bay. Field observations and laboratory experiments showed that the species is more common in algae-dominated sandy habitats, where it improves water clarity and increases dissolved oxygen through bioturbation. However, exposure to very high temperatures caused rapid mortality, suggesting that marine heat waves could threaten this species and the ecological functions it provides.

One-third of the world's people do not have access to clean drinking water. Nadella and Nadella tackle this issue by testing a low-cost filtration system for removing heavy metal and bacteria from water.

Duckweed plays an important role in its aquatic environment by removing pollutants, such as zinc, from the water. In this study, the authors demonstrate that uptake of zinc by duckweed is inhibited by the presence of oil in the water, but this effect can be reversed with the addition of a dispersing agent.