اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMapping the Landscape of COVID-19 Crisis Visualizations
83
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact peoples attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research.
قيم البحث
اقرأ أيضاً
Behavioral gender differences are known to exist for a wide range of human activities including the way people communicate, move, provision themselves, or organize leisure activities. Using mobile phone data from 1.2 million devices in Austria (15% o
f the population) across the first phase of the COVID-19 crisis, we quantify gender-specific patterns of communication intensity, mobility, and circadian rhythms. We show the resilience of behavioral patterns with respect to the shock imposed by a strict nation-wide lock-down that Austria experienced in the beginning of the crisis with severe implications on public and private life. We find drastic differences in gender-specific responses during the different phases of the pandemic. After the lock-down gender differences in mobility and communication patterns increased massively, while sleeping patterns and circadian rhythms tend to synchronize. In particular, women had fewer but longer phone calls than men during the lock-down. Mobility declined massively for both genders, however, women tend to restrict their movement stronger than men. Women showed a stronger tendency to avoid shopping centers and more men frequented recreational areas. After the lock-down, males returned back to normal quicker than women; young age-cohorts return much quicker. Differences are driven by the young and adolescent population. An age stratification highlights the role of retirement on behavioral differences. We find that the length of a day of men and women is reduced by one hour. We discuss the findings in the light of gender-specific coping strategies in response to stress and crisis.
Crises such as natural disasters, global pandemics, and social unrest continuously threaten our world and emotionally affect millions of people worldwide in distinct ways. Understanding emotions that people express during large-scale crises helps inf
orm policy makers and first responders about the emotional states of the population as well as provide emotional support to those who need such support. We present CovidEmo, ~1K tweets labeled with emotions. We examine how well large pre-trained language models generalize across domains and crises in the task of perceived emotion prediction in the context of COVID-19. Our results show that existing models do not directly transfer from one disaster type to another but using labeled emotional corpora for domain adaptation is beneficial.
Set systems are used to model data that naturally arises in many contexts: social networks have communities, musicians have genres, and patients have symptoms. Visualizations that accurately reflect the information in the underlying set system make i
t possible to identify the set elements, the sets themselves, and the relationships between the sets. In static contexts, such as print media or infographics, it is necessary to capture this information without the help of interactions. With this in mind, we consider three different systems for medium-sized set data, LineSets, EulerView, and MetroSets, and report the results of a controlled human-subjects experiment comparing their effectiveness. Specifically, we evaluate the performance, in terms of time and error, on tasks that cover the spectrum of static set-based tasks. We also collect and analyze qualitative data about the three different visualization systems. Our results include statistically significant differences, suggesting that MetroSets performs and scales better.
The impact of COVID-19 on students has been enormous, with an increase in worries about fiscal and physical health, a rapid shift to online learning, and increased isolation. In addition to these changes, students with disabilities/health concerns ma
y face accessibility problems with online learning or communication tools, and their stress may be compounded by additional risks such as financial stress or pre-existing conditions. To our knowledge, no one has looked specifically at the impact of COVID-19 on students with disabilities/health concerns. In this paper, we present data from a survey of 147 students with and without disabilities collected in late March to early April of 2020 to assess the impact of COVID-19 on these students education and mental health. Our findings show that students with disabilities/health concerns were more concerned about classes going online than their peers without disabilities. In addition, students with disabilities/health concerns also reported that they have experienced more COVID-19 related adversities compared to their peers without disabilities/health concerns. We argue that students with disabilities/health concerns in higher education need confidence in the accessibility of the online learning tools that are becoming increasingly prevalent in higher education not only because of COVID-19 but also more generally. In addition, educational technologies will be more accessible if they consider the learning context, and are designed to provide a supportive, calm, and connecting learning environment.
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolatio
n and mental health. We introduce textit{Expressive Interviewing}--an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the systems design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
