Opinion de-polarization of social networks with GNNs

• URL: http://arxiv.org/abs/2412.09404v1
• Date: Thu, 12 Dec 2024 16:09:50 GMT
• Title: Opinion de-polarization of social networks with GNNs
• Authors: Konstantinos Mylonas, Thrasyvoulos Spyropoulos,
• Abstract summary: We propose an efficient algorithm to identify a good set of K users, such that if they adopt a moderate stance around a topic, the polarization is minimized. Our algorithm employs a Graph Neural Network and thus it can handle large graphs more effectively than other approaches.
• Score: 9.49192088119451
• License:
• Abstract: Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests that social media are highly polarized. A phenomenon that is commonly observed is the echo chamber structure, where users are organized in polarized communities and form connections only with similar-minded individuals, limiting themselves to consume specific content. In this paper we explore a way to decrease the polarization of networks with two echo chambers. Particularly, we observe that if some users adopt a moderate opinion about a topic, the polarization of the network decreases. Based on this observation, we propose an efficient algorithm to identify a good set of K users, such that if they adopt a moderate stance around a topic, the polarization is minimized. Our algorithm employs a Graph Neural Network and thus it can handle large graphs more effectively than other approaches

Related papers

• Dynamics of Ideological Biases of Social Media Users [0.0]
  We show that the evolution of online platform-wide opinion groups is driven by the desire to hold popular opinions. We focus on two social media: Twitter and Parler, on which we tracked the political biases of their users.
  arXiv  Detail & Related papers  (2023-09-27T19:39:07Z)
• Adversaries with Limited Information in the Friedkin--Johnsen Model [25.89905526128351]
  In recent years, online social networks have been the target of adversaries who seek to introduce discord into societies. We present approximation algorithms for detecting a small set of users who are highly influential for the disagreement and polarization in the network.
  arXiv  Detail & Related papers  (2023-06-17T10:32:02Z)
• Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit [9.875731068651561]
  We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context. We analyze user interactions across five years in the r/news community on Reddit.
  arXiv  Detail & Related papers  (2023-02-15T11:37:12Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Reddit [66.09950457847242]
  We study the activity of 5.97M Reddit users and 421M comments posted over 13 years. We create a typology of relationships between political communities based on whether their users are toxic to each other.
  arXiv  Detail & Related papers  (2022-11-25T22:17:07Z)
• Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods [0.0]
  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.
  arXiv  Detail & Related papers  (2022-07-27T21:23:27Z)
• Perspective-taking to Reduce Affective Polarization on Social Media [11.379010432760241]
  We deploy a randomized field experiment through a browser extension to 1,611 participants on Twitter. We find that simply exposing participants to "outgroup" feeds enhances engagement, but not an understanding of why others hold their political views. framing the experience in familiar, empathic terms by prompting participants to recall a disagreement does not affect engagement, but does increase their ability to understand opposing views.
  arXiv  Detail & Related papers  (2021-10-11T20:25:10Z)
• Reaching the bubble may not be enough: news media role in online political polarization [58.720142291102135]
  A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations. This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
  arXiv  Detail & Related papers  (2021-09-18T11:34:04Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Political audience diversity and news reliability in algorithmic ranking [54.23273310155137]
  We propose using the political diversity of a website's audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards.
  arXiv  Detail & Related papers  (2020-07-16T02:13:55Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

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.