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

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.

Network polarization, filter bubbles, and echo chambers: an annotated review of measures and reduction methods

“…Grossetti et al. (2020, 2021) studied communities on a large Twitter dataset to quantify how recommendation systems affect users' behavior, and how content personalization can increase the echo chamber effect and create filter bubbles. A preliminary study was conducted to detect a filter bubble effect on users' community profiles, proposing a community‐aware model whose objective is to reduce the filter‐bubble impact.…”

“…They performed an empirical study of their methods on synthetic and two real-world datasets (Twitter and Reddit), finding that there is space to reduce both controversy and disagreement in real-world networks. Grossetti et al (2020Grossetti et al ( , 2021 studied communities on a large Twitter dataset to quantify how recommendation systems affect users' behavior, and how content personalization can increase the echo chamber effect and create filter bubbles. A preliminary study was conducted to detect a filter bubble effect on users' community profiles, proposing a community-aware model whose objective is to reduce the filter-bubble impact.…”

Network polarization, filter bubbles, and echo chambers: An annotated review of measures, models, and case studies

“…Grossetti et al [62,63] studied communities on a large Twitter dataset to quantify how recommendation systems affect users' behavior, and how content personalization can increase the echo chamber effect and create filter bubbles. A preliminary study was conducted to detect a filter bubble effect on users' community profiles, proposing a community-aware model whose objective is to reduce the filter-bubble impact.…”