RENOIR ProjectI participated in the RENOIR project, which aimed to reverse-engineer the dynamics of social network information. This involved analyzing the spread of innovations and rumors among people, the viral spreading of information in social media such as Twitter or Facebook, and the dynamics of news and their topics across time.
As part of the project, I led a work package that involved collaborating with top schools like Stanford University, Rensselaer Polytechnic Institute, Nanyang Technological University Singapore, Carnegie Mellon University, Jozef Stefan Institute, and more. We worked closely together to develop innovative solutions to understand the dynamics of social network information and how it spreads.
Our research involved various activities, including gathering and analyzing data, developing machine learning models, and testing and validating our solutions. By leveraging the expertise of our international team of researchers, we created innovative solutions that had a significant impact on the social network information dynamics field.
This project was an excellent opportunity to showcase the power of collaboration and innovation in research. By working together with top schools around the world, we were able to create solutions that have the potential to revolutionize our understanding of social network information dynamics. As a researcher, I leveraged my data science and machine learning expertise to contribute to this groundbreaking research.