ANR XTCOVIF: COVID19 Social and Economic impact

• ANR
  XTCOVIF
• Semantic
  Shift
• Predicting the Spread
  of COVID-19
• Trust in
  Government
• Racism and
  COVID-19
• Population
  Mobility
• Publications and
  Datasets

ANR-Project XTCOVIF

Title: Exploring Twitter streams for Social and Economic impact of COVID in France.

Acronym: XTCOVIF

Project coordinator: Michalis Vazirgiannis, LIX (Laboratoire d’Informatique de l’École polytechnique)

Project duration: 12 Months

Starting Date: 01/09/2020

Summary: Social media have promoted new forms of social interaction, and have changed the way people communicate and interact. People use social media to report the latest news, but also to express their opinions and feelings about real-world events. Users show particular interest in emergency situations such as natural disasters and pandemics. With the worldwide spread of the COVID-19 infection, individual activity on social media platforms has increased. Each day, users post millions of messages related to the pandemic.
In this project, we study the social and economic impacts of COVID-19 using data obtained from social media. We focus on 6 different societal issues related to the outbreak of COVID-19, namely (1) people’s sentiments and emotions, (2) the decline of tourism, (3) the trust that citizens show in governments, (4) the evolution of language, (5) the increase in racism and xenophobia, and (6) the impact of COVID-19 on population mobility.
The project will provide significant socio-economic contributions. Specifically, the outcomes of the proposed research could be valuable in policy planning and management, allowing the French government to gain a clearer picture of the situation and adopt better measures for the prevention of infection as well as for improving citizens’ well-being. Certainly, these tools and results will be useful in the case of a second wave of the pandemic.
Τhe project will also create valuable resources which will allow researchers and policymakers to study the COVID-19 crisis from a social perspective and to analyze the human behavior and information spreading during the pandemic.

Keywords:data mining and social media, natural language processing, social impact, deep learning, covid19, France