Roni Barak Ventura

Firearm injuries are a leading cause of death in the United States, surpassing fatalities from motor vehicle crashes. Despite this significant public health risk, Americans continue to purchase firearms in large quantities. Commonly cited drivers of firearm acquisition include fear of violent crime, fear of mass shootings, and panic-buying. Additionally, advocacy groups’ activity on social media may capitalize on emotions like fear and influence firearm acquisition. The simultaneous effects of these variables have not been explored in a causal framework. In this study, we aim to elucidate the causal roles of media coverage of firearm laws and regulations, media coverage of mass shootings, media coverage of violent crimes, and the Twitter activity of anti- and proregulation advocacy groups in short-term firearm acquisition in the United States. We collect daily time series for these variables from 2012 to 2020 and employ the PCMCI+ framework to investigate the causal structures among them simultaneously. Our results indicate that the Twitter activity of antiregulation advocacy groups directly drives firearm acquisitions. We also find that media coverage of firearm laws and regulations and media coverage of violent crimes influence firearm acquisition. Although media coverage of mass shootings and online activity of proregulation organizations are potential drivers of firearm acquisition, in the short term, only the lobbying efforts of antiregulation organizations on social media and specific media coverage appear to influence individuals’ decisions to purchase firearms.

In the realm of urban science, scaling laws are essential for understanding the relationship between city population and urban features, such as socioeconomic outputs. Ideally, these laws would be based on complete datasets; however, researchers often face challenges related to data availability and reporting practices, resulting in datasets that include only the highest observations of the urban features (top-k). A key question that emerges is: Under what conditions can an analysis based solely on top-k observations accurately determine whether a scaling relationship is truly superlinear or sublinear? To address this question, we conduct a numerical study that explores how relying exclusively on reported values can lead to erroneous conclusions, revealing a selection bias that favors sublinear over superlinear scaling. In response, we develop a method that provides robust estimates of the minimum and maximum potential scaling exponents when only top-k observations are available. We apply this method to two case studies involving firearm violence, a domain notorious for its suppressed datasets, and we demonstrate how this approach offers a reliable framework for analyzing scaling relationships with censored data.

Public sentiment towards the police is a matter of great interest in the United States, as reports on police misconduct are increasingly being published in mass and social media. Here, we test how the public’s perception of the police can be majorly shaped by media reports of police brutality and local crime. We collect data on media coverage of police brutality and local crime, together with Twitter posts from 2010-2020 about the police in 18 metropolitan areas in the country. Using a range of model-free approaches building on transfer entropy analysis, we discover an association between public sentiment towards the police and media coverage of police brutality. We cautiously interpret this relationship as causal. Through this lens, the public’s sentiment towards the police appears to be driven by media-projected images of police misconduct, with no statistically significant evidence for a comparable effect driven by media reports on crimes.

BACKGROUND Firearm injuries are a leading cause of death in the United States, where a greater number of people die by firearms than in motor vehicle crashes. Although firearms present a significant public health risk, Americans continue to purchase them in large amounts. Three drivers of firearm acquisition are commonly cited in the literature, namely, fear of violent crime, fear of mass shootings, and panic-buying. These drivers were widely researched as conceptual factors that contribute to individuals’ decision to purchase firearms, yet their role in prompting purchases has never been studied. In addition, the activity of relevant interest groups on social media, which capitalize on emotions like fear, may impact impulsive firearm acquisition. The combined roles of all factors in driving firearm purchases have never been tested in a causal framework. OBJECTIVE We aim to elucidate the role of media coverage of firearm laws and regulations, mass shootings, violent crimes, and the activity of anti- and pro-regulation organizations on social media in short-term firearm acquisition in the United States. METHODS We generate daily time series of media coverage of firearm laws and regulations, media coverage of mass shootings, media coverage of violent crimes, Twitter posts of anti-regulation organizations, Twitter posts of pro-regulation organizations, and background checks between 2012 and 2020. We employ the framework of PCMCI+ to investigate the causal structures between the six variables simultaneously. RESULTS We find that the activity of anti-regulation organizations on social media directly drives firearm acquisitions. We also show that media coverage of firearm laws and regulations and media coverage of violent crimes influence firearm acquisition. CONCLUSIONS Although media coverage of mass shootings and online activity of pro-regulation organizations are possible drivers of firearm acquisition, in the short term, only lobbying efforts of anti-regulation organizations, media coverage of violent crime, and media coverage of firearm laws and regulations appear to influence people’s decision to purchase a firearm. CLINICALTRIAL

Mass shootings are becoming more frequent in the United States, as we routinely learn from the media about attempts that have been prevented or tragedies that destroyed entire communities. To date, there has been limited understanding of the modus operandi of mass shooters, especially those who seek fame through their attacks. Here, we explore whether the attacks of these fame-seeking mass shooters were more surprising than those of others and clarify the link between fame and surprise in mass shootings. We assembled a dataset of 189 mass shootings from 1966 to 2021, integrating data from multiple sources. We categorized the incidents in terms of the targeted population and shooting location. We measured “surprisal” (often known as “Shannon information content”) with respect to these features, and we scored fame from Wikipedia traffic data—a commonly used metric of fame. Surprisal was significantly higher for fame-seeking mass shooters than non-fame-seeking ones. We also registered a significant positive correlation between fame and surprisal controlling for the number of casualties and injured victims. Not only do we uncover a link between fame-seeking behavior and surprise in the attacks but also we demonstrate an association between the fame of a mass shooting and its surprise.

Freshwater ecosystems are among the most biodiverse ecosystems on the planet and constitute key natural resources for human economies. These ecosystems, however, are endangered due to persistent human activities, ranging from temperature changes to water pollution. Here, we develop an online serious game to educate youth regarding human impact on freshwater ecosystems. In particular, we focus on how environmental variables can affect the behavior and well-being of freshwater fish, which are critical to the food web and the function of the whole ecosystem. In the game, players aim to maximize fish fitness by foraging and reproducing, despite adversarial environmental conditions. We leverage dynamical systems theory to create mathematical models for all the game characters and components, including animal locomotion and hydrodynamic interactions with the flow physics of a virtual river. We test the educational value and usability of the game on a group of middle-school students from New York City. Our results suggest that games like the proposed serious game could be a viable tool to engage youth in environmental education about freshwater ecosystems.

Background: Sustained engagement is essential for the success of telerehabilitation programs. However, patients’ lack of motivation and adherence could undermine these goals. To overcome this challenge, physical exercises have often been gamified. Building on the advantages of serious games, we propose a citizen science–based approach in which patients perform scientific tasks by using interactive interfaces and help advance scientific causes of their choice. This approach capitalizes on human intellect and benevolence while promoting learning. To further enhance engagement, we propose performing citizen science activities in immersive media, such as virtual reality (VR). Objective: This study aims to present a novel methodology to facilitate the remote identification and classification of human movements for the automatic assessment of motor performance in telerehabilitation. The data-driven approach is presented in the context of a citizen science software dedicated to bimanual training in VR. Specifically, users interact with the interface and make contributions to an environmental citizen science project while moving both arms in concert. Methods: In all, 9 healthy individuals interacted with the citizen science software by using a commercial VR gaming device. The software included a calibration phase to evaluate the users’ range of motion along the 3 anatomical planes of motion and to adapt the sensitivity of the software’s response to their movements. During calibration, the time series of the users’ movements were recorded by the sensors embedded in the device. We performed principal component analysis to identify salient features of movements and then applied a bagged trees ensemble classifier to classify the movements. Results: The classification achieved high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was the most accurately classified movement (99.2%), and horizontal shoulder abduction to the right side of the body was the most misclassified movement (98.8%). Conclusions: Coordinated bimanual movements in VR can be classified with high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in VR-mediated telerehabilitation.