Tag Archives: Social Network Analysis

13/11/2018 – Talk by prof. Dieter Fiems

Title:  Modelling group dynamics in epidemic opinion propagation
Time: 13:00
Location: Meeting Room B, Building Zeta
Type: Research talk
Speaker:  Dieter Fiems
Abstract:  Motivated by weblogs and discussion forums, epidemic opinion propagation on affiliation networks is investigated. An affiliation network is a bi-partite graph describing the connections between individuals and their affiliations. In contrast to epidemics on complex networks, the epidemic spreading process in the current setting is not the consequence of pairwise interactions among individuals but of a group dynamic. We derive a Markov model for the epidemic process and its fluid limit obtained by sending the population size to infinity while keeping the number of affiliations constant. This results in a set of modified SIR-like ordinary differential equations. Different types of group dynamics are studied numerically and the accuracy of the fluid limit is verified by simulation.

15/02/2017 – Talk by Fabiana Zollo

Title: Social Dynamics on Online Social Media: A Data Science Approach
Time: 13:00
Location: Meeting room, Building Zeta
Type: Research Result
Speaker: Fabiana Zollo
Information, rumors, debates shape and reinforce the perception of reality and heavily impact public opinion. Indeed, the way in which individuals influence each other is one of the foundational challenges in several disciplines such as sociology, social psychology, and economics. In particular, on online social networks users tend to select information that is coherent to their system of beliefs and to form polarized groups of like-minded people –i.e, echo chambers– where they reinforce and polarize their pre-existing opinions. Such a context exacerbates misinformation, which has traditionally represented a political, social, and economic risk. In this talk we explore how we can understand social dynamics by analyzing massive data on Facebook. By means of a tight quantitative analysis on 376 millions users we characterize the anatomy of news consumption on a global scale. We show that users tend to focus on a limited set of pages (selective exposure) eliciting a sharp and polarized community structure among news outlets. Moreover, we find similar patterns around the Brexit –the British referendum to leave the European Union– debate, where we observe the spontaneous emergence of two well segregated and polarized groups of users around news oultets. Our findings provide interesting insights about the determinants of polarization and the evolution of core narratives on online debating, and highlight the crucial role of data science techniques to understand and map the information space on online social media. The main aim of this research stream is to identify non-trivial proxies for the early detection of massive (mis)informational cascades. Furthermore, by combining users traces we are able to draft the main concepts and beliefs of the core narrative of an echo chamber and its related perceptions.