Maximizing the Diversity of Exposure in a Social Network
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
IEEE Transactions on Knowledge and Data Engineering, Volume 34, issue 9, pp. 4357-4370
Abstract
Social-media platforms have created new ways for citizens to stay informed and participate in public debates. However, to enable a healthy environment for information sharing, social deliberation, and opinion formation, citizens need to be exposed to sufficiently diverse viewpoints that challenge their assumptions, instead of being trapped inside filter bubbles. In this paper, we propose a novel approach to maximize the diversity of exposure in a social network. We formulate the problem in the context of information propagation, as a task of recommending a small number of news articles to selected users. We take into account content and user leanings, and the probability of further sharing an article. Our model allows us to capture the balance between maximizing the spread of information and ensuring the exposure of users to diverse viewpoints. The resulting problem can be cast as maximizing a monotone and submodular function subject to a matroid constraint on the allocation of articles to users. It is a challenging generalization of the influence-maximization problem. Yet, we are able to devise scalable approximation algorithms by introducing a novel extension to the notion of random reverse-reachable sets. We experimentally demonstrate the efficiency and scalability of our algorithm on several real-world datasets.Description
| openaire: EC/H2020/654024/EU//SoBigData
Other note
Citation
Matakos, A, Aslay, C, Galbrun, E & Gionis, A 2022, 'Maximizing the Diversity of Exposure in a Social Network', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 9, pp. 4357-4370. https://doi.org/10.1109/TKDE.2020.3038711