About Us


The Network Contagion Research Institute (NCRI) is a 501(c) organization (nonprofit) that deploys machine learning tools to expose hate on digital social networks within a cross-platform, public-minded, and global framework. We are a multidisciplinary group of scientists and engineers who apply our technical skills to further public insight into the problem of online hate. We examine how hateful images and language grow within and between Web communities and how the infection of hate spreads between the online and the real world.

The condition of hate afflicts our capacity to see ourselves clearly, speak plainly to one another, and to assume collective and personal responsibility for our conditions.


Joel Finkelstein, Director, Co-founder

Joel Finkelstein holds a Ph.D. in Psychology Princeton University, and founding fellow at the Center for Compassion (CCARE) at Stanford University, where his research focused on the neural underpinnings of compassion and social behavior. Finkelstein was a graduate fellow at the National Science Foundation as well as an experimental philosopher in the field of human agency, free will, and the relationship between humanity and technology. Formerly at Google, he has headed charitable and philanthropic events, established meditation courses and also organized and spoken at international conferences on the psychology and neuroscience of compassion.


Jeremy Blackburn, Board Member, Co-founder

Blackburn is Assistant Professor in the Computer Science Department at UAB. He studies anti-social actors on the Internet and has received media coverage in Nature, The Atlantic, the BBC, Vice, New Scientist, and MIT Technology Review, among others. Although his foundations are in large-scale distributed systems, he has spent most of his time measuring and understanding bad behavior on the world’s largest distributed system, the World Wide Web. His research has ranged from studying how cheating behavior spreads like a disease through a global network of online video game players, to understanding and predicting toxic behavior in the world’s most popular multiplayer video game, and more recently, understanding online hate speech, harassment campaigns, and the influence of fringe Web communities through the lens of fake news. In addition, Blackburn has published more traditional Computer Science topics such as middle box enabling cryptographic protocols, privacy preserving Web surfing technologies, detection of Web trackers, performance of mobile applications, Software Defined Networks, measuring the adoption of new Web protocols, and understanding human perception of Web page performance. Prior to joining UAB, Blackburn spent three years as an Associate Researcher at Telefonica Research in Barcelona, Spain. While at Telefonica, he led an Innovation team focused on making use of Software Defined Networks and the Cloud to improve the performance of mobile devices. He co-founded the International Data-driven Research for Advanced Modelling and Analysis Lab, an international group of scientists focusing on modern socio-technical issues with expertise ranging from low level cryptography to video games. He has served on the technical program committee for WWW ’16, WWW ’17, WWW ’18, and MobiSys ’16 among others, and organized several workshops and a Dagstuhl seminar on video games and Cybersafety. Previously, he was a Principal Developer who worked in Content Distribution Systems and Digital Rights Management. He built software and services with tens of thousands of retail customers, generating in excess of $1M per year in revenue.


Savvas Zannettou, Graduate Research Fellow

Savvas Zannettou is a PhD Candidate from Cyprus University of Technology. In 2014 and 2016 he received respectively the BSc and MSc degrees in Computer Engineering from Cyprus University of Technology. During 2014, he was a Research Intern at NEC Labs Europe for 6 months where he worked on Software-Defined Networks. During 2017 and 2018, he was a Research Intern at Telefonica Research for 12 months. In his research, he applies machine learning and data-driven quantitative analysis to understand emerging phenomena on the Web like the spread of false information  and the spread of hateful rhetoric.