Hostname: page-component-76d6cb85b7-ntvhh Total loading time: 0 Render date: 2026-07-12T18:24:40.390Z Has data issue: false hasContentIssue false

Visibility and exploitation in social networks

Published online by Cambridge University Press:  19 December 2023

Rustam Galimullin
Affiliation:
Department of Information Science and Media Studies, University of Bergen, Bergen, Norway
Mina Young Pedersen*
Affiliation:
Department of Information Science and Media Studies, University of Bergen, Bergen, Norway
*
Corresponding author: Mina Young Pedersen; Email: mina.pedersen@uib.no
Rights & Permissions [Opens in a new window]

Abstract

Social media is not a neutral channel. How visible information posted online is depends on many factors such as the network structure, the emotional volatility of the content, and the design of the social media platform. In this paper, we use formal methods to study the visibility of agents and information in a social network, as well as how vulnerable the network is to exploitation. We introduce a modal logic to reason about a social network of agents that can follow each other, post, and share information. We show that by imposing some simple rules on the system, a potentially malicious agent can take advantage of the network construction to post an unpopular opinion that may reach many agents. The network is presented both in static and dynamic forms. We prove completeness, expressivity, and model checking problem complexity results for the corresponding logical systems.

Information

Type
Special Issue: WoLLIC 2022
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Figure 1. Model M with the followership relation depicted by dashed arrows.

Figure 1

Table 1. Hybrid and bidirectional axioms

Figure 2

Table 2. Followership and visibility axioms

Figure 3

Figure 2. A follower-network M where vaccination is a controversial topic.

Figure 4

Figure 3. Update $M^{a:v}$ after agent a posts in favor of vaccines.

Figure 5

Figure 4. Update $M^{a:d}$ after agent a posts on dogs.

Figure 6

Figure 5. Update $M^{a:d,a:v}$ after agent a posts on vaccination after first posting on dogs.

Figure 7

Figure 6. Models M and N.

Figure 8

Figure 7. Models $M^{a:p}$ and $N^{a:p}$. Reflexive $p_a$-arrows and followership arrows from $b_k$ to a for $k \in \{1, ..., n\}$ are omitted for readability.

Figure 9

Algorithm 1 An algorithm for model checking VL

Figure 10

Figure 8. Models M, $M^{a:p_1}$, $M^{a:q}$, and $M^{a:p_1, a:p_2}$.

Figure 11

Algorithm 2 An algorithm for model checking AVL