Type: Internship

3 posts

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User confinement in online communities (SNA and info-viz)

Computer Science: Two possible internships Context User confinement (or containment) in online communities – variously denoted as, inter alia, balkanization, bubbles, echo chambers, fragmentation – is at the core of a growing number of studies. In the framework of Algodiv and Socsemics, the team contributes to advancing the formalization and the empirical appraisal of the informational and interactional confinement of individuals in web communities. This internship would aim at either appraising or visualizing user confinement on Twitter and its topical subnetworks, building upon exploratory work previously achieved within the team. Goals There are two main directions for further research at […]

Algorithmic guidance on cultural good consumption (online/offline)

Computer Science: One possible internship Context The effects of recommendation algorithms on the access to information and cultural goods is at the center of a growing debate, which aims at assessing whether they rather contribute to enlarge or to restrain the horizon of users with respect to their “organic” behavior, i.e. absent algorithms. Goals This internship topic focuses on the impact of algorithmic guidance on cultural good consumption, specifically musical goods. It aims at addressing the following question: to what extent could we say that traditional musical “recommendation” (radios, music libraries, record stores) is more or less diversified and/or serendipous […]

Automatic Hypothesis Generation for Network Growth Models

Computer Science: Two possible internships Context Networks have become a fundamental abstraction for modeling systems across many scientific fields. Plausible hypothesis describing their growth processes can help us understand a wide range of phenomena, but formulating such hypothesis is often challenging and requires insights that may be counter-intuitive. In the last years, we have developed an approach to automatically discover realistic network growth models from empirical data, employing a machine learning technique inspired by natural selection, and defining a unified formalism to describe such models as a mathematical function of arbitrary complexity [1]. As the proposed method is completely general […]