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Control Algorithm Design for Fairness in Social Media
In the context of social network analysis, whether the purpose is spreading news, a product or a specific technology, the network structure plays a key role in the propagation phenomenon.
Individuals getting the information, or the product as first (early-adopters) are in a privileged position, and they play a key role in determining who will receive the information at the end of the spreading process. This translates into the fact that selecting the early adopters in order to maximize the outreach is strategical choice. This optimization problem is known in the literature as social influence maximization. Social influence maximization is relevant in understanding how information propagates in a network of individuals. Many algorithms determining the optimal set of early adopters have been proposed. However, most of them are only based on network measurements such as distance centrality or nodes’ degree and do not consider individuals’ information such as gender, ethnicity, geographical location, thus leading to undesired glass ceiling effects. This information however is of great importance whenever one wants to ensure that all categories in the initial dataset are equitably represented in the final outcome. In these cases, social influence maximization must be juxtaposed with the concept of fairness. The scientific literature related to this field witnesses how automated and greedy algorithms amplify inequalities among different demographic groups and what it means to be fair in information diffusion is still an open question.
Keywords: Fairness in social media
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Giulia De Pasquale (degiulia@ethz.ch), Dr. Nicolo' Pagan (nicolo.pagan@uzh.ch), Ezzat Elokda (elokdae@ethz.ch)
Giulia De Pasquale (degiulia@ethz.ch), Dr. Nicolo' Pagan (nicolo.pagan@uzh.ch), Ezzat Elokda (elokdae@ethz.ch)