We study human behavior through digital traces with methods from complexity science, as part of the interdisciplinary field of Computational Social Science.
Some of our research lines are the following:

Computational Affective Science

We combine big social data and computational modeling to understand affective life and emotional well-being.
Example publications:
Collective Emotions and Social Resilience in the Digital Traces After a Terrorist Attack. Psychological Science
The Dynamics of Emotions in Online Interaction. Royal Society Open Science

Complex Privacy

We analyze online social networks discover how the actions of others affect the control we have on our data.
Example publications:
Leaking Privacy and Shadow Profiles in Online Social Networks. Science Advances
Collective aspects of privacy in the Twitter social network. EPJ Data Science

Online Inequality

We study how online media capture and create inequalities in society at large.
Example publications:
Analyzing gender inequality through large-scale Facebook advertising data. PNAS
Quantifying the Economic and Cultural Biases of Social Media through Trending Topics. PLoS ONE

Polarization Dynamics

We analyze the origins and consequences of opinion polarization and social fragmentation.
Example publications:
Ideological and Temporal Components of Network Polarization in Online Political Participatory Media. Policy & Internet
Social Signals and Algorithmic Trading of Bitcoin. Royal Society Open Science