Behavioral Digital Trace Data in Response to the COVID-19 Pandemic

The main topic of the 1st Social ComQuant Summer School will be the analysis of behavioral digital trace data. Digital traces such as social media posts, search engine queries, mobile phone records, and co-purchases are increasingly allowing social and computational scientists the analysis of human behavior at high spatial and temporal resolution.

The COVID-19 pandemic will provide the context of the summer school. Public health advice, stay-at-home orders, and self-imposed limitations have dramatically changed human behaviors on a global scale. For the first time in an epidemic, changes in human behavior have been monitored through digital platforms in real-time. Digital trace data has provided epidemiologists, public health officials, and social scientists with new tools to study the impact of interventions against the virus and how society is changing during the course of the pandemic.

The aim of the summer school is to showcase different examples of how behavioral digital trace data from various sources (social media, location data, news, participatory surveillance) have been used in the fight of COVID-19 and in understanding how society is being affected by the consequences of the pandemic.

A group of outstanding keynote speakers and lecturers will teach different methods to capture, analyze and interpret digital trace data and how these can help investigate the interplay between human dynamics and the epidemic spread. 

Topics of the lectures will include:

  • Human mobility from digital traces
  • Social media and the spread of misinformation
  • Search engine query data 
  • Online surveys through social media marketing platforms
  • Digital contact tracing

The lectures will focus on methodological approaches and previous experience in epidemiology or public health is not required to attend the School. 



Application deadline: May 14, 2021

Notification: June 8, 2021

School dates: 26-31 July 2021


The summer school will take place online and activities will be scheduled between 10 AM and 5 PM CEST. 

The schedule will include lectures and hands-on projects. Students will conduct small projects in which they will apply the newly learned methods under the lecturers’ supervision. 

Projects will be focused on the analysis of data streams from different sources. 


The summer school comes with no costs.


The summer school is open to anyone interested in learning about methods of Computational Social Science. Students from any discipline are invited to apply. 

Previous coding experience is not strictly required but students are encouraged to become familiar with a scripting language like Python or R before joining the summer school. 

The aim is to bring together a diverse international group of people. However, we have a limited number of spots for participants. That means everyone needs to apply.

Please upload the following documents (in one single pdf) at Easychair (link below) and attach:

Motivation Letter (one-page max)

CV and Publication List


The applicants should submit their application (CV+motivation letter) as a single PDF file using the button "submit paper", as it was a conference paper.

There will be one author (name and affiliation of the applicant).

Title, abstract, and keywords can be "Summer School application".


Dr. Manlio De Domenico
Dr. Laetitia Gauvin
Dr. Maimuna Majumder
Dr. Dina Mistry
Dr. Elisa Omodei
Dr. Daniela Perrotta
Dr. Marcel Salathé
Dr. Samuel Scarpino
Dr. Aleksandra Urman
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