Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction

Estimating traffic disruption patterns with volunteer geographic information

Resumen de la comunicación[EN] Accurate understanding and forecasting of traffic conditions is a key contemporary problem for local policymakers. Road networks are increasingly congested, yet data on usage patterns is often scarce or expensive to obtain, meaning that informed policy decision-making is difficult. This paper explores the extent to which traffic disruption can be estimated from static features of the volunteer geographic information site OpenStreetMap [OSM]. Kernel Density Estimates of OSM features are used as predictors for a linear regression of counts of traffic incidents at 6,500 separate points within the Oxfordshire road traffic network. For highly granular points of just 10m2, it is shown that more than half of variation in traffic outcomes can be explained with these static features alone. Furthermore, use of OSM’s granular point of interest data improves considerably on more aggregate categories which are typically used in studies of transportation and land use. Although the estimations are by no means perfect, they offer a good baseline model considering the data is free to obtain and easy to process.This project was supported by funding from InnovateUK under grant number 52277-393176, the NERC under grant number NE/N00728X/1, and the Lloyd’s Register Foundation.Bright, J.; Camargo, C.; Hale, S.; Mcneill, G.; Raman, S. (2018). Estimating traffic disruption patterns with volunteer geographic information. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 252-252. https://doi.org/10.4995/CARMA2018.2018.8319OCS25225

Building a Model of Collaboration Between Historically Black and Historically White Universities

Despite increases over the last two decades in the number of degrees awarded to students from underrepresented groups in science, technology, engineering, and mathematics (STEM) disciplines, enhancing diversity in these disciplines remains a challenge. This article describes a strategic approach to this challenge—the development of a collaborative partnership between two universities: the historically Black Elizabeth City State University and the historically White University of New Hampshire. The partnership, a type of learning organization built on three mutually agreed upon principles, strives to enhance opportunities for underrepresented students to pursue careers in the STEM disciplines. This article further describes six promising practices that framed the partnership, which resulted in the submission of nine proposals to federal agencies and the funding of four grants that led to the implementation, research, learning, and evaluation that followed

Physician Executive Leadership: Student-Led Curriculum to Fill Gaps in Traditional Medical Education

Students at Sidney Kimmel Medical College (SKMC) have identified a gap in the traditional medical curriculum surrounding topics such as telehealth, the patient experience, health policy, medical malpractice, and health care entrepreneurship and innovation, and in response have initiated a student-centered, student-led, student-driven program called Physician Executive Leadership (PEL). PEL provides students with a variety of avenues to engage with these topics, such as lectures from leaders in each of these fields, easy access to weekly news articles on current events in health care, targeted review sessions on the US health care system, and the opportunity to voice and develop ideas through an online publication. To identify the gaps in medical education PEL is best suited to fill, we administered a survey to 174 students at Sidney Kimmel Medical College.The survey contained 20 multiple-choice questions to assess general knowledge on health insurance and reimbursement, health care policy and reform, and care quality and patient experience. It also included a subjective self-assessment of students’ understanding of and interest in these topics. Overall, we found that although the traditional medical school curriculum improved students\u27 understanding of these topics from year to year, it is not sufficient on it\u27s own: on average, students failed to achieve a passing score of 70% in any of the categories tested. Further illustrating the importance of this program, students self-identified a gap between their current level of understanding and what they want to know. Please visit our website www.physicianexecutiveleadership.com to learn more!https://jdc.jefferson.edu/pel/1000/thumbnail.jp

Contrasting Responses of Rhizosphere Bacterial, Fungal, Protist and Nematode Communities to Nitrogen Fertilisation and Crop Genotype in Field Grown Oilseed Rape (Brassica napus)

© 2021 Picot, Hale, Hilton, Teakle, Schäfer, Huang, Perryman, West and Bending. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/The rhizosphere microbiome is considered to play a key role in determining crop health. However, current understanding of the factors which shape assembly and composition of the microbiome is heavily biased toward bacterial communities, and the relevance for other microbial groups is unclear. Furthermore, community assembly is determined by a variety of factors, including host genotype, environment and agricultural management practices, and their relative importance and interactions remain to be elucidated. We investigated the impact of nitrogen fertilization on rhizosphere bacterial, fungal, nematode and protist communities of 10 contrasting oilseed rape genotypes in a field experiment. We found significant differences in the composition of bacteria, fungi, protist and nematode communities between the rhizosphere and bulk soil. Nitrogen application had a significant but weak effect on fungal, bacterial, and protist community composition, and this was associated with increased relative abundance of a complex of fungal pathogens in the rhizosphere and soil, including Mycosphaerella sp. and Leptosphaeria sp. Network analysis showed that nitrogen application had different effects on microbial community connectivity in the soil and rhizosphere. Crop genotype significantly affected fungal community composition, with evidence for a degree of genotype specificity for a number of pathogens, including L. maculans, Alternaria sp., Pyrenopeziza brassicae, Olpidium brassicae, and L. biglobosa, and also potentially beneficial Heliotales root endophytes. Crop genotype had no significant effect on assembly of bacteria, protist or nematode communities. There was no relationship between genetic distance of crop genotypes and the extent of dissimilarity of rhizosphere microbial communities. Field disease assessment confirmed infection of crops by Leptosphaeria sp., P. brassicae, and Alternaria sp., indicating that rhizosphere microbiome sequencing was an effective indicator of plant health. We conclude that under field conditions soil and rhizosphere nutrient stoichiometry and crop genotype are key factors determining crop health by influencing the infection of roots by pathogenic and mutualistic fungal communities, and the connectivity and stability of rhizosphere microbiome interaction networks.Peer reviewedFinal Published versio

Stokes imaging polarimetry using image restoration at the Swedish 1-m Solar Telescope

Aims: We aim to achieve high spatial resolution as well as high polarimetric sensitivity, using an earth-based 1m-class solar telescope, for the study of magnetic fine structure on the Sun. Methods: We use a setup with 3 high-speed, low-noise cameras to construct datasets with interleaved polarimetric states, particularly suitable for Multi-Object Multi-Frame Blind Deconvolution image restorations. We discuss the polarimetric calibration routine as well as various potential sources of error in the results. Results: We obtained near diffraction limited images, with a noise level of approximately 10^(-3) I(cont). We confirm that dark-cores have a weaker magnetic field and at a lower inclination angle with respect to the solar surface than the edges of the penumbral filament. We show that the magnetic field strength in faculae-striations is significantly lower than in other nearby parts of the faculae.Comment: Accepted for publication in Astronomy & Astrophysics, 12 pages, 11 figure