Navigating Decentralized Online Social Networks: An Overview of   Technical and Societal Challenges in Architectural Choices
        - URL: http://arxiv.org/abs/2504.00071v1
 - Date: Mon, 31 Mar 2025 17:39:55 GMT
 - Title: Navigating Decentralized Online Social Networks: An Overview of   Technical and Societal Challenges in Architectural Choices
 - Authors: Ujun Jeong, Lynnette Hui Xian Ng, Kathleen M. Carley, Huan Liu, 
 - Abstract summary: Decentralized online social networks have evolved from experimental stages to operating at unprecedented scale.<n>We examine four major architectures: federated, peer-to-peer, blockchain, and hybrid.<n>By linking these architectural aspects to real-world cases, our work provides a foundation for understanding the societal implications of decentralized social platforms.
 - Score: 16.22174363457934
 - License: http://creativecommons.org/licenses/by/4.0/
 - Abstract:   Decentralized online social networks have evolved from experimental stages to operating at unprecedented scale, with broader adoption and more active use than ever before. Platforms like Mastodon, Bluesky, Hive, and Nostr have seen notable growth, particularly following the wave of user migration after Twitter's acquisition in October 2022. As new platforms build upon earlier decentralization architectures and explore novel configurations, it becomes increasingly important to understand how these foundations shape both the direction and limitations of decentralization. Prior literature primarily focuses on specific architectures, resulting in fragmented views that overlook how different social networks encounter similar challenges and complement one another. This paper fills that gap by presenting a comprehensive view of the current decentralized online social network landscape. We examine four major architectures: federated, peer-to-peer, blockchain, and hybrid, tracing their evolution and evaluating how they support core social networking functions. By linking these architectural aspects to real-world cases, our work provides a foundation for understanding the societal implications of decentralized social platforms. 
 
       
      
        Related papers
        - A Collectivist, Economic Perspective on AI [65.268245109828]
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before.<n>This view neglects the fact that humans are social animals, and that much of our intelligence is social and cultural in origin.<n>The path forward is not merely more data and compute, but a thorough blending of economic and social concepts with computational and inferential concepts.
arXiv  Detail & Related papers  (2025-07-08T03:07:43Z) - Bridging the Narrative Divide: Cross-Platform Discourse Networks in   Fragmented Ecosystems [9.119607936530038]
Political discourse increasingly fragmented across different social networks.<n>To understand how narratives traverse fragmented ecosystems, we offer a structural lens for anticipating how narratives traverse ecosystems.<n>These findings offer implications for crossplatform governance, content moderation, and policy interventions.
arXiv  Detail & Related papers  (2025-05-22T16:53:52Z) - Characterizing the Fragmentation of the Social Media Ecosystem [39.58317527488534]
We use a dataset of 126M URLs posted by nearly 6M users on nine social media platforms.
We find a clear separation between mainstream and alt-tech platforms.
These findings outline the main dimensions defining the fragmentation and polarization of the social media ecosystem.
arXiv  Detail & Related papers  (2024-11-25T18:45:03Z) - Architecture for Protecting Data Privacy in Decentralized Social   Networks [5.874802930380899]
This paper proposes a novel Decentralized Social Network employing comprehensive technology and Decentralized Networks completed by Access Control Smart Contracts.
In conclusion, the principal results highlight the benefit of our decentralized social network to protect user privacy.
arXiv  Detail & Related papers  (2024-09-27T00:35:02Z) - Community Shaping in the Digital Age: A Temporal Fusion Framework for   Analyzing Discourse Fragmentation in Online Social Networks [45.58331196717468]
This research presents a framework for analyzing the dynamics of online communities in social media platforms.
By combining text classification and dynamic social network analysis, we uncover mechanisms driving community formation and evolution.
arXiv  Detail & Related papers  (2024-09-18T03:03:02Z) - Decentralized Social Networks and the Future of Free Speech Online [4.249974621573213]
Decentralized social networks like Mastodon and BlueSky are trending topics that have drawn much attention and discussion in recent years.
This article critically assesses the decentralization project's prospect for communications online.
It uses normative theories of free speech to examine whether and how the decentralization design could facilitate users' freedom of expression online.
arXiv  Detail & Related papers  (2024-06-11T04:18:53Z) - Social Intelligence Data Infrastructure: Structuring the Present and   Navigating the Future [59.78608958395464]
We build a Social AI Data Infrastructure, which consists of a comprehensive social AI taxonomy and a data library of 480 NLP datasets.
Our infrastructure allows us to analyze existing dataset efforts, and also evaluate language models' performance in different social intelligence aspects.
We show there is a need for multifaceted datasets, increased diversity in language and culture, more long-tailed social situations, and more interactive data in future social intelligence data efforts.
arXiv  Detail & Related papers  (2024-02-28T00:22:42Z) - Grassroots Social Networking: Where People have Agency over their   Personal Information and Social Graph [2.06682776181122]
We present a grassroots architecture for serverless, permissionless, peer-to-peer social networks termed Grassroots Social Networking.
The architecture incorporates (i) a decentralized social graph, where each person controls, maintains and stores only their local neighborhood in the graph.
We provide two example Grassroots Social Networking protocols -- Twitter-like and WhatsApp-like -- and address their security (safety, liveness and privacy), spam/bot/deep-fake resistance, and implementation.
arXiv  Detail & Related papers  (2023-06-24T11:43:17Z) - Self-supervised Hypergraph Representation Learning for Sociological
  Analysis [52.514283292498405]
We propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria.
First, we propose an effective hypergraph awareness and a fast line graph construction framework.
Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users.
arXiv  Detail & Related papers  (2022-12-22T01:20:29Z) - Detecting Ideal Instagram Influencer Using Social Network Analysis [0.0]
The paper focuses on social network analysis (SNA) for a real-world online marketing strategy.
The study contributes by comparing various centrality measures to identify the most central nodes in the network and uses a linear threshold model to understand the spreading behaviour of individual users.
arXiv  Detail & Related papers  (2021-07-12T20:53:58Z) - PHASE: PHysically-grounded Abstract Social Events for Machine Social
  Perception [50.551003004553806]
We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions.
Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events.
As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
arXiv  Detail & Related papers  (2021-03-02T18:44:57Z) - 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) - DiffNet++: A Neural Influence and Interest Diffusion Network for Social
  Recommendation [50.08581302050378]
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences.
We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet)
In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
arXiv  Detail & Related papers  (2020-01-15T08:45:34Z) 
        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.