Contact Person: Dr. Iza Moise
Duration: 09/2015 - present
Funding: EU Horizon 2020 (grant agreement No. 654024)
Social Mining & Big Data Ecosystem
SoBigData proposes to create the Social Mining & Big Data Ecosystem: a research infrastructure (RI) providing an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life, as recorded by “big data”. Building on several established national infrastructures, SoBigData will open up new research avenues in multiple research fields, including mathematics, ICT, and human, social and economic sciences, by enabling easy comparison, re-use and integration of state-of-the-art big social data, methods, and services, into new research. The high-level goal of SoBigData is to deliver an innovative research infrastructure for analysing large volumes of social data, by bringing together state-of-the-art methods and services from complementary research fields.
Our team will contribute to the project by 1) developing tools to monitor and analyse the social processes around data
science and innovation, 2) enhancing these social processes by integrating personalization techniques and recommender systems, 3) providing methods and services for measuring and understanding social behaviour and human dynamics (such as individual profiling, learning and monitoring collective sentiment/opinion, predictive analytics for social measurements, models for multi-dimensional diffusion processes).
S. Wang, I. Moise, D. Helbing and T. Terano (2017). Early Signals of Trending Rumor Event in Streaming Social Media. Workshop of IEEE Computer Society Signature Conference on Computers, Software and Applications (COMPSAC) 2017, Turin, Italy.
D. Griego, V. Buff, E. Hayoz, I. Moise, E. Pournaras (2017). Sensing and Mining Urban Qualities in Smart Cities. Proceedings of the 31st IEEE International Conference on Advanced Information Networking and Applications-AINA 2017, Taipei.
I. Moise, E. Gaere, R. Merz, S. Koch and E. Pournaras (2016). Tracking Language Mobility in the Twitter Landscape. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, 663-670.
N. Antulov-Fantulin, D. Tolic (2016). Time augmented bond percolation mapping of spreading dynamics on networks. arXiv preprint arXiv:1612.08629.
E. Pournaras, J. Nikolic, P. Velásquez, M. Trovati, N. Bessis, D. Helbing (2016). Self-regulatory information sharing in participatory social sensing. EPJ Data Science, 5(1), 14.