Maximizing the Diversity of Exposure in a Social Network

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 9; pp. 4357 - 4370
• Main Authors: Matakos, Antonis, Aslay, Cigdem, Galbrun, Esther, Gionis, Aristides
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Approximation algorithms; Artificial Intelligence; Computer Science; Cultural differences; diversity maximization; Exposure; filter bubbles; Greedy algorithms; Information propagation; Maximization; Optimization; Resource management; social influence; Social networking (online); Social networks; Task analysis
• ISSN: 1041-4347; 1558-2191; 2326-3865; 1558-2191
• DOI: 10.1109/TKDE.2020.3038711

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.