Manuel Cebrian focuses on Computer security, Scale, Social network, Econometrics and Social media. As a member of one scientific family, Manuel Cebrian mostly works in the field of Computer security, focusing on Emergency management and, on occasion, The Internet. He studies Social network, focusing on Friendship paradox in particular.
His Econometrics study incorporates themes from Dependency, Empirical evidence, Function and Hierarchy. His work in Social media addresses subjects such as Information Dissemination, which are connected to disciplines such as Natural disaster. His Key study combines topics in areas such as Cognitive psychology, Local area network, Data mining and Process.
The scientist’s investigation covers issues in Artificial intelligence, Social media, Social network, Data science and Computer security. His work in Artificial intelligence tackles topics such as Theoretical computer science which are related to areas like Algorithm. His Social media study integrates concerns from other disciplines, such as Information Dissemination, Power, Popularity, Crisis communication and Internet privacy.
His Social network research includes elements of Key and Data mining. His study explores the link between Data science and topics such as Crowdsourcing that cross with problems in Dilemma and Crowds. His study in The Internet extends to Computer security with its themes.
Manuel Cebrian spends much of his time researching Artificial intelligence, Social media, Machine learning, Data science and Cultural diversity. His biological study spans a wide range of topics, including Cooperative behavior and Identification. His studies in Social media integrate themes in fields like Extreme events, Crisis communication, Power and Internet privacy.
His Machine learning study which covers Biometrics that intersects with Feature vector, Function and Tree. Manuel Cebrian has included themes like Normative, Network science, Complex network and Set in his Data science study. Manuel Cebrian studied Resilience and Computer security that intersect with Social network.
His primary areas of study are Artificial intelligence, Social media, Power, Scale and Crisis communication. As part of his studies on Artificial intelligence, Manuel Cebrian often connects relevant areas like Machine learning. His Machine learning research is multidisciplinary, relying on both Tree and Function.
While working on this project, Manuel Cebrian studies both Social media and Event. The concepts of his Power study are interwoven with issues in Digital divide, Network effect, Interpersonal relationship and Demographic economics. The various areas that Manuel Cebrian examines in his Crisis communication study include Emergency management, Economy and Internet privacy.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Rapid assessment of disaster damage using social media activity.
Yury Kryvasheyeu;Yury Kryvasheyeu;Haohui Chen;Haohui Chen;Nick Obradovich;Nick Obradovich;Esteban Moro.
Science Advances (2016)
Time-Critical Social Mobilization
Galen Pickard;Wei Pan;Iyad Rahwan;Iyad Rahwan;Manuel Cebrian.
Social sensing for epidemiological behavior change
Anmol Madan;Manuel Cebrian;David Lazer;Alex Pentland.
ubiquitous computing (2010)
Sensing the "Health State" of a Community
A. Madan;M. Cebrian;S. Moturu;K. Farrahi.
IEEE Pervasive Computing (2012)
Limited communication capacity unveils strategies for human interaction
Giovanna Miritello;Rubén Lara;Manuel Cebrian;Manuel Cebrian;Esteban Moro;Esteban Moro.
Scientific Reports (2013)
Toward understanding the impact of artificial intelligence on labor
Morgan R. Frank;David Autor;James E. Bessen;Erik Brynjolfsson;Erik Brynjolfsson.
Proceedings of the National Academy of Sciences of the United States of America (2019)
Urban characteristics attributable to density-driven tie formation
Wei Pan;Gourab Ghoshal;Gourab Ghoshal;Coco Krumme;Manuel Cebrian;Manuel Cebrian;Manuel Cebrian.
Nature Communications (2013)
Social media fingerprints of unemployment.
Alejandro Llorente;Manuel Garcia-Herranz;Manuel Cebrian;Esteban Moro.
PLOS ONE (2015)
Reflecting on the DARPA Red Balloon Challenge
John C. Tang;Manuel Cebrian;Nicklaus A. Giacobe;Hyun-Woo Kim.
Communications of The ACM (2011)
COMMON PITFALLS USING THE NORMALIZED COMPRESSION DISTANCE: WHAT TO WATCH OUT FOR IN A COMPRESSOR
Manuel Alfonseca;Manuel Cebrián;Alfonso Ortega.
Communications in information and systems (2005)
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