Tell me something my friends do not know
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Conference article in proceedings
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Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
327-336
Series
2018 IEEE International Conference on Data Mining (ICDM), IEEE International Conference on Data Mining (ICDM)
Abstract
Social media have a great potential to improve information dissemination in our society, yet, they have been held accountable for a number of undesirable effects, such as polarization and filter bubbles. It is thus important to understand these negative phenomena and develop methods to combat them. In this paper we propose a novel approach to address the problem of breaking filter bubbles in social media. We do so by aiming to maximize the diversity of the information exposed to connected social-media users. We formulate the problem of maximizing the diversity of exposure as a quadratic-knapsack problem. We show that the proposed diversity-maximization problem is inapproximable, and thus, we resort to polynomial non-approximable algorithms, inspired by solutions developed for the quadratic knapsack problem, as well as scalable greedy heuristics. We complement our algorithms with instance-specific upper bounds, which are used to provide empirical approximation guarantees for the given problem instances. Our experimental evaluation shows that a proposed greedy algorithm followed by randomized local search is the algorithm of choice given its quality-vs.-efficiency trade-off.Description
| openaire: EC/H2020/654024/EU//SoBigData
Keywords
Diversity maximization, Filter bubble, Quadratic knapsack
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Citation
Matakos , A & Gionis , A 2018 , Tell me something my friends do not know : Diversity maximization in social networks . in 2018 IEEE International Conference on Data Mining, ICDM 2018 . , 8594857 , IEEE International Conference on Data Mining (ICDM) , IEEE , pp. 327-336 , IEEE International Conference on Data Mining , Singapore , Singapore , 17/11/2018 . https://doi.org/10.1109/ICDM.2018.00048
