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Reducing the filter bubble effect on Twitter by considering communities for recommendations

Quentin Grossetti (Cedric Lab, Conservatoire National des Arts et Métiers, Paris, France)
Cedric du Mouza (Cedric Lab, Conservatoire National des Arts et Métiers, Paris, France)
Nicolas Travers (Léonard de Vinci Pôle Universitaire, Research Center, Paris La Défense, France)
Camelia Constantin (LIP6, Sorbonne University, Paris, France)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 4 October 2021

Issue publication date: 1 December 2021

497

Abstract

Purpose

Social network platforms are considered today as a major communication mean. Their success leads to an unprecedented growth of user-generated content; therefore, finding interesting content for a given user has become a major issue. Recommender systems allow these platforms to personalize individual experience and increase user engagement by filtering messages according to user interest and/or neighborhood. Recent research results show, however, that this content personalization might increase the echo chamber effect and create filter bubbles that restrain the diversity of opinions regarding the recommended content.

Design/methodology/approach

The purpose of this paper is to present a thorough study of communities on a large Twitter data set that quantifies the effect of recommender systems on users’ behavior by creating filter bubbles. The authors further propose their community-aware model (CAM) that counters the impact of different recommender systems on information consumption.

Findings

The authors propose their CAM that counters the impact of different recommender systems on information consumption. The study results show that filter bubbles effects concern up to 10% of users and the proposed model based on the similarities between communities enhance recommendations.

Originality/value

The authors proposed the CAM approach, which relies on similarities between communities to re-rank lists of recommendations to weaken the filter bubble effect for these users.

Keywords

Citation

Grossetti, Q., du Mouza, C., Travers, N. and Constantin, C. (2021), "Reducing the filter bubble effect on Twitter by considering communities for recommendations", International Journal of Web Information Systems, Vol. 17 No. 6, pp. 728-752. https://doi.org/10.1108/IJWIS-06-2021-0065

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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