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

• URL: http://arxiv.org/abs/2207.13799v5
• Date: Wed, 5 Apr 2023 03:44:17 GMT
• Title: Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods
• Authors: Ruben Interian, Ruslan G. Marzo, Isela Mendoza, Celso C. Ribeiro
• Abstract summary: Polarization arises when the underlying network becomes characterized by highly connected groups with weak inter-group connectivity. This work presents an annotated review of network polarization measures and models used to handle the polarization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Polarization arises when the underlying network connecting the members of a community or society becomes characterized by highly connected groups with weak inter-group connectivity. The increasing polarization, the strengthening of echo chambers, and the isolation caused by information filters in social networks are increasingly attracting the attention of researchers from different areas of knowledge such as computer science, economics, social and political sciences. This work presents an annotated review of network polarization measures and models used to handle the polarization. Several approaches for measuring polarization in graphs and networks were identified, including those based on homophily, modularity, random walks, and balance theory. The strategies used for reducing polarization include methods that propose edge or node editions (including insertions or deletions, as well as edge weight modifications), changes in social network design, or changes in the recommendation systems embedded in these networks.

Related papers

• A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs [5.285847977231642]
  Measuring group polarization on social media presents several challenges that have not yet been addressed by existing solutions. We designed a solution based on a multi-agent system and used a graph-structured Community Sentiment Network (CSN) to represent polarization states. In summary, the proposed approach has significant value in terms of usability, accuracy, and interpretability.
  arXiv  Detail & Related papers  (2024-11-19T03:29:17Z)
• Ethos and Pathos in Online Group Discussions: Corpora for Polarisation Issues in Social Media [6.530320465510631]
  Growing polarisation in society caught the attention of the scientific community as well as news media. We propose to approach the problem by investigating rhetorical strategies employed by individuals in polarising discussions online. We develop multi-topic and multi-platform corpora with manual annotation of appeals to ethos and pathos, two modes of persuasion in Aristotelian rhetoric.
  arXiv  Detail & Related papers  (2024-04-07T09:10:47Z)
• Mitigating Framing Bias with Polarity Minimization Loss [56.24404488440295]
  Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias.
  arXiv  Detail & Related papers  (2023-11-03T09:50:23Z)
• PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection [81.16859686137435]
  We present PARTNER, a novel 3D object detector in the polar coordinate. Our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on and ONCE validation set.
  arXiv  Detail & Related papers  (2023-08-08T01:59:20Z)
• Fooling Polarization-based Vision using Locally Controllable Polarizing Projection [55.40484331029597]
  We warn the community of the vulnerability of polarization-based vision, which can be more serious than RGB-based vision. By adapting a commercial LCD projector, we achieve locally controllable polarizing projection, which is successfully utilized to fool state-of-the-art polarization-based vision algorithms.
  arXiv  Detail & Related papers  (2023-03-31T08:48:57Z)
• A Systematic Evaluation of Node Embedding Robustness [77.29026280120277]
  We assess the empirical robustness of node embedding models to random and adversarial poisoning attacks. We compare edge addition, deletion and rewiring strategies computed using network properties as well as node labels. We found that node classification suffers from higher performance degradation as opposed to network reconstruction.
  arXiv  Detail & Related papers  (2022-09-16T17:20:23Z)
• Local Edge Dynamics and Opinion Polarization [17.613690272861053]
  We study how local edge dynamics can drive opinion polarization. We introduce a variant of the classic Friedkin-Johnsen opinion dynamics, augmented with a simple time-evolving network model. We show that our model is tractable to theoretical analysis, which helps explain how these local dynamics erode connectivity across opinion groups.
  arXiv  Detail & Related papers  (2021-11-28T01:59:57Z)
• Designing Recommender Systems to Depolarize [0.32634122554913997]
  Polarization is implicated in the erosion of democracy and the progression to violence. While algorithm-driven social media does not seem to be a primary driver of polarization at the country level, it could be a useful intervention point in polarized societies. This paper examines algorithmic depolarization interventions with the goal of conflict transformation.
  arXiv  Detail & Related papers  (2021-07-11T03:23:42Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph [54.03786611989613]
  signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) We develop a unified framework, termed as Signed Infomax Hyperbolic Graph (textbfSIHG) In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion.
  arXiv  Detail & Related papers  (2020-11-25T05:09:03Z)
• Polarization Model of Online Social Networks Based on the Concept of Spontaneous Symmetry Breaking [3.084629788740097]
  It is necessary to understand the mechanism of polarization to establish technologies that can counter polarization. This paper introduces a fundamental model for understanding polarization that is based on the concept of spontaneous symmetry breaking.
  arXiv  Detail & Related papers  (2020-11-10T21:03:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.