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

arXiv: https://arxiv.org/abs/2109.10462 · HTML: https://mlsanity.com/paper/2109.10462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [False Information on Web and Social Media: A Survey](https://mlsanity.com/paper/1804.08559/) (2018)
- [FIVR: Fine-grained Incident Video Retrieval](https://mlsanity.com/paper/1809.04094/) (2018)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Network structure from rich but noisy data](https://mlsanity.com/paper/1703.07376/) (2017)
- [Network Backboning with Noisy Data](https://mlsanity.com/paper/1701.07336/) (2017)
- [Community Discovery in Dynamic Networks: a Survey](https://mlsanity.com/paper/1707.03186/) (2017)
- [Anatomy of an online misinformation network](https://mlsanity.com/paper/1801.06122/) (2018)
- [#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate](https://mlsanity.com/paper/1802.09808/) (2018)
- [Misinformation spreading on Facebook](https://mlsanity.com/paper/1706.09494/) (2017)
- [Do Cascades Recur?](https://mlsanity.com/paper/1602.01107/) (2016)
- [The Impact of Projection and Backboning on Network Topologies](https://mlsanity.com/paper/1906.09081/) (2019)
- [Can WhatsApp Counter Misinformation by Limiting Message Forwarding?](https://mlsanity.com/paper/1909.08740/) (2019)
- [#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds](https://mlsanity.com/paper/1510.00249/) (2015)
- [Multi-resolution community detection in massive networks](https://mlsanity.com/paper/1612.04668/) (2016)
- [Defending Elections Against Malicious Spread of Misinformation](https://mlsanity.com/paper/1809.05521/) (2018)
- [Characterizing Attention Cascades in WhatsApp Groups](https://mlsanity.com/paper/1905.00825/) (2019)
- [On the Dynamics of Political Discussions on Instagram: A Network Perspective](https://mlsanity.com/paper/2109.09152/) (2021)

## 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.