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Social networks as sensors

Temporal, structural and activity patterns in social networks are related to human behavior. Thus, networks do have different shape and dynamics under different exogenous and endogenous shocks or conditions. In the last years we have addressed the question of whether we can use our understanding of social networks to anticipate, predict or measure important phenomena as information propagation, disaster damage, unemployment shocks, weather conditions, gender digital divide, etc.

For example in a series of papers, we demonstrated for the first time the ability of the so-called “friendship paradox” in social networks to get better collection of users (sensors) that can anticipate meme, news or event-related information propagation in networks. Even those sensors can be user to disaster management as we found during Hurricane Sandy.

We also found that social media behaviors like mobility around economical areas (see figure), diversity of interactions between areas or the gramatical correctness of posts are deeply related to unemployment in those areas. Those findings have been corroborated in other countries, showing the universal character of those fingerprints of socio-economic status in social media. In the last years we have also used data from social media to understand gender inequality. By developing a unique large-scale dataset from Facebook, we have seen the relation between the activity of 1.4 billion users in 217 countries, the digital gender gap and other inequality measures related to education, health or economic opportunity. This research was published in PNAS and together with the methodology in previous papers we have use these results in a collaboration with UNICEF to measure Human Development Indexes in third world countries, where official data is scarce or incomplete.

Some recent papers:

  • Analyzing gender inequality through large-scale Facebook advertising data
    David Garcia, Yonas Mitike Kassa, Angel Cuevas, Manuel Cebrian, Esteban Moro, Iyad Rahwan, Ruben Cuevas
    Proceedings Of The National Academy Of Sciences 115 6958–6963 (2018)
    PDF Journal Altmetric: 203

  • Social Media Fingerprints of Unemployment
    Alejandro Llorente, Manuel Garcia-Herranz, Manuel Cebrian, Esteban Moro
    PLoS ONE 10 e0128692 (2015)
    PDF Journal Altmetric: 325

  • Using friends as sensors to detect global-scale contagious outbreaks.
    Manuel Garcia-Herranz, Esteban Moro, Manuel Cebrian, Nicholas A Christakis, James H Fowler
    PLoS ONE 9 e92413– (2014)
    PDF Journal Altmetric: 251

  • Rapid assessment of disaster damage using social media activity
    Yury Kryvasheyeu, Haohui Chen, Nick Obradovich, Esteban Moro, Pascal Van Hentenryck, James Fowler, Manuel Cebrian
    Science Advances 2 e1500779 (2016)
    PDF Journal Altmetric: 521





Esteban Moro

Professor at Universidad Carlos III de Madrid and MIT Medialab. Working on Complex Systems, Social Networks and Urban Science.