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Special talk by Cigdem Aslay on Maximizing the Diversity of Information Exposure in an Online Social Network

2020.01.27 | Søs Küster Markussen

Date Tue 11 Feb
Time 09:00 10:00
Location 5342-333 Åbogade 34, 8200 Aarhus N


Maximizing the Diversity of Information Exposure in an Online Social Network


In this talk I will present a novel approach to contribute towards bursting filter bubbles in online social networks. We formulate the problem as a task of recommending news articles to selected users with the aim to maximize the overall diversity of information exposure in a social network. We consider a realistic setting where we take into account the political leanings of users and articles, and the probability of users to further share articles. This setting allows us to strike a balance between maximizing the spread of articles and ensuring the exposure of users to diverse viewpoints. We show that this problem is a challenging generalization of the influence maximization problem, which is NP-hard, and can be cast as maximizing a monotone submodular function subject to a matroid constraint on the allocation of articles to users. We introduce the notion of random reverse co-exposure sets and a set of estimation techniques based on martingales for efficiently estimating the expected diversity of exposure. Accordingly, we devise a scalable instantiation of the greedy algorithm that provides (1/2-epsilon)-approximation to the optimal solution with high probability. Experimentally, we demonstrate the scalability and efficiency of our algorithm on several real-world social networks.


Cigdem Aslay is a postdoctoral researcher in the Data Mining Group of the Computer Science Department at Aalto University. She received her PhD from Universitat Pompeu Fabra in December 2016. During her PhD, she was hosted by Yahoo Labs in Barcelona as a research intern in the Web Mining Group. In 2017, she was a postdoctoral researcher in the Algorithmic Data Analytics Lab at ISI Foundation. Her work focuses on algorithmic methods for graph mining and social network analysis, with an emphasis on information propagation in online social networks.

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