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Elected officials have privileged roles in public communication. In contrast to national politicians, whose posting content is more likely to be closely scrutinized by a robust ecosystem of nationally focused media outlets, sub-national politicians are more likely to openly disseminate harmful content with limited media scrutiny. In this paper, we analyze the factors that explain the online visibility of over 6.5K unique state legislators in the US and how their visibility might be impacted by posting low-credibility or uncivil content. We conducted a study of posting on Twitter and Facebook (FB) during 2020-21 to analyze how legislators engage with users on these platforms. The results indicate that distributing content with low-credibility information attracts greater attention from users on FB and Twitter for Republicans. Conversely, posting content that is considered uncivil on Twitter receives less attention. A noticeable scarcity of posts containing uncivil content was observed on FB, which may be attributed to the different communication patterns of legislators on these platforms. In most cases, the effect is more pronounced among the most ideologically extreme legislators. Our research explores the influence exerted by state legislators on online political conversations, with Twitter and FB serving as case studies. Furthermore, it sheds light on the differences in the conduct of political actors on these platforms. This study contributes to a better understanding of the role that political figures play in shaping online political discourse. 

This paper investigates deploying connected and automated vehicle (CAV) lanes in transportation networks with a focus on measuring and preserving equity among travelers. A new metric is proposed to characterize equity based on (1) generalized travel cost per unit origin-destination (OD) distance for travelers on each OD pair and using each vehicle type and (2) maximum deviation of the standardized unit generalized travel cost from system average. A bi-level bi-objective program is developed to simultaneously minimize system travel cost and inequity while deploying CAV lanes. A solution algorithm that combines nondominated sorting genetic algorithm II and variable neighborhood search is designed. Through extensive numerical experiments, we find (1) inequity is more prominent when travel demand is high; (2) human-driven vehicle travelers become more disadvantageous with lower CAV price and higher CAV automation; and (3) subsidy is effective in mitigating inequity, but a fee for using CAV lanes is less promising. 

Abstract Electromagnetic ion cyclotron (EMIC) waves are commonly observed in the Earth's magnetosphere and play a significant role in regulating relativistic electron fluxes. The waveform of EMIC waves comprises amplitude‐modulated wave packets, known as “subpackets.” Despite their prevalence, the underlying physics and associated particle dynamics for subpacket formation remain poorly understood. In this study, using Van Allen Probe A observations, we present several rising‐tone EMIC wave events to reveal the downward frequency chirping between adjacent subpackets. By performing a hybrid simulation, we demonstrate for the first time that these wave properties are associated with the oscillation of proton holes in the wave gyrophase space induced by cyclotron resonance. The oscillation modulates the energy transfer between waves and particles, establishing a direct link between subpacket formation in cyclotron waves and nonlinear wave‐particle interactions. This new understanding advances our knowledge of subpacket formation in general and its broader implications in space plasma physics. 

Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, at both the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.