ABSTRACT
The COVID-19 pandemic initially caused worldwide concerns about food insecurity. Tweets analyzed in real-time may help food assistance providers target food supplies to where they are most urgently needed. In this exploratory study, we use natural language processing to extract sentiments and emotions expressed in food security-related tweets early in the pandemic in U.S. states. The emotion joy dominated in these tweets nationally, but only anger, disgust, and fear were also statistically correlated with contemporaneous food insufficiency rates reported in the Household Pulse Survey; more nuanced and statistically stronger correlations are detected within states, including a negative correlation with joy.
ABSTRACT
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of ( i ) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and ( ii ) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.
ABSTRACT
The world is facing enormous challenges, ranging from climate change to extreme poverty. The 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs) were adopted by United Nations Member States in 2015 as an operational framework to address these challenges. The SDGs include No Poverty, Quality Education, Gender Equality, Peace, Justice and Strong Institutions, among others, as well as a meta goal on Partnerships for the Goals. Despite limitations, the SDGs form a data have also become apparent in the context of, for example, the Sudan revolution (public sentiment) or the Beirut explosion in August 2020 (infrastructure damage). The paucity of data has been highlighted during the COVID-19 pandemic, with its sudden impact on all aspects of life, most of which have yet to be quantified. Going beyond availability, accessibility, and timeliness, there is a need for more disaggregated data, such as by gender and town. These challenges provide an opportunity to use non-traditional data sources as a complement to existing data and approaches, in particular through the use of artificial intelligence (AI).