mlsanityData to 7 Oct 2026

A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp

arXiv 2109.10462 ↗First posted 22 Sep 2021cs.SI cs.AI cs.CYcited by 0

Not on the map. Why?

Abstract (arXiv)

WhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries. In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation on the platform.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 64 references that are in the map. First posted on arXiv 22 Sep 2021.

Builds on

21 of 64 references.

References · 21

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2020DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detectioncited by 231
  4. 2016Community detection in networks: A user guidecited by 105
  5. 2018False Information on Web and Social Media: A Surveycited by 33
  6. 2018FIVR: Fine-grained Incident Video Retrievalcited by 26
  7. 2018Trends in the Diffusion of Misinformation on Social Mediacited by 25
  8. 2017Network structure from rich but noisy datacited by 17
  9. 2017Network Backboning with Noisy Datacited by 11
  10. 2017Community Discovery in Dynamic Networks: a Surveycited by 8
  11. 2018Anatomy of an online misinformation networkcited by 8
  12. 2018#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debatecited by 7
9 more
  1. 2017Misinformation spreading on Facebookcited by 4
  2. 2016Do Cascades Recur?cited by 3
  3. 2019The Impact of Projection and Backboning on Network Topologiescited by 3
  4. 2019Can WhatsApp Counter Misinformation by Limiting Message Forwarding?cited by 2
  5. 2015#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compoundscited by 1
  6. 2016Multi-resolution community detection in massive networkscited by 1
  7. 2018Defending Elections Against Malicious Spread of Misinformationcited by 1
  8. 2019Characterizing Attention Cascades in WhatsApp Groupscited by 1
  9. 2021On the Dynamics of Political Discussions on Instagram: A Network Perspectivecited by 1

Built on by

Not cited yet.