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Maximizing the Diversity of Exposure in a Social Network
Aalto Univ, Dept Comp Sci, Espoo 02150, Finland..
Aarhus Univ, Dept Comp Sci, DK-8000 Aarhus, Denmark..
Univ Eastern Finland, Sch Comp, Kuopio 70210, Finland..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS. Aalto Univ, Dept Comp Sci, Helsinki 02150, Finland..ORCID iD: 0000-0002-5211-112X
2022 (English)In: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 34, no 9, p. 4357-4370Article in journal (Refereed) Published
Abstract [en]

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 take a step in this direction and 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. In the proposed setting, we take into account content and user leanings, and the probability of further sharing an article. Our model allows 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 34, no 9, p. 4357-4370
Keywords [en]
Cultural differences, Social networking (online), Approximation algorithms, Computer science, Task analysis, Resource management, Greedy algorithms, Information propagation, diversity maximization, filter bubbles, social influence
National Category
Computer Sciences Media Studies
Identifiers
URN: urn:nbn:se:kth:diva-316442DOI: 10.1109/TKDE.2020.3038711ISI: 000836626800022Scopus ID: 2-s2.0-85097142228OAI: oai:DiVA.org:kth-316442DiVA, id: diva2:1688259
Note

QC 20220818

Available from: 2022-08-18 Created: 2022-08-18 Last updated: 2025-01-27Bibliographically approved

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Gionis, Aristides

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