Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts

A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts' rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback-Leibler and Jensen-Shannon divergences. Through several case studies, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives.

Exposure to urban parks improves affect and reduces negativity on Twitter

Urbanization and the decline of access to nature have coincided with a rise of mental health problems. A growing body of research has demonstrated an association between nature contact and improved mental affect (i.e., mood). However, previous approaches have been unable to quantify the benefits of urban greenspace exposure and compare how different types of outdoor public spaces impact mood. Here, we use Twitter to investigate how mental affect varies before, during, and after visits to a large urban park system. We analyze the sentiment of tweets to estimate the magnitude and duration of the affect benefit of visiting parks. We find that affect is substantially higher during park visits and remains elevated for several hours following the visit. Visits to Regional Parks, which are greener and have greater vegetative cover, result in a greater increase in affect compared to Civic Plazas and Squares. Finally, we analyze the words in tweets around park visits to explore several theorized mechanisms linking nature exposure with mental and cognitive benefits. Negation words such as "no", "not", and "don't" decrease in frequency during visits to urban parks. These results point to the most beneficial types of nature contact for mental health benefits and can be used by urban planners and public health officials to improve the well-being of growing urban populations.