Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread

• URL: http://arxiv.org/abs/2401.12509v2
• Date: Wed, 24 Jan 2024 01:56:12 GMT
• Title: Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread
• Authors: Prateek Puri, Gabriel Hassler, Anton Shenk, Sai Katragadda
• Abstract summary: We develop a simulation framework for studying misinformation spread within online social networks. We create a 'digital clone' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop a simulation framework for studying misinformation spread within online social networks that blends agent-based modeling and natural language processing techniques. While many other agent-based simulations exist in this space, questions over their fidelity and generalization to existing networks in part hinders their ability to provide actionable insights. To partially address these concerns, we create a 'digital clone' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users. We parse these histories to both extract the structure of the network and model the nuanced ways in which information is shared and spread among its members. Unlike many other agent-based methods in this space, information sharing between users in our framework is sensitive to topic of discussion, user preferences, and online community dynamics. To evaluate the fidelity of our method, we seed our cloned network with a set of posts recorded in the base network and compare propagation dynamics between the two, observing reasonable agreement across the twin networks over a variety of metrics. Lastly, we explore how the cloned network may serve as a flexible, low-cost testbed for misinformation countermeasure evaluation and red teaming analysis. We hope the tools explored here augment existing efforts in the space and unlock new opportunities for misinformation countermeasure evaluation, a field that may become increasingly important to consider with the anticipated rise of misinformation campaigns fueled by generative artificial intelligence.

Related papers

• Leveraging advances in machine learning for the robust classification and interpretation of networks [0.0]
  Simulation approaches involve selecting a suitable network generative model such as Erd"os-R'enyi or small-world. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes.
  arXiv  Detail & Related papers  (2024-03-20T00:24:23Z)
• Stepping out of Flatland: Discovering Behavior Patterns as Topological Structures in Cyber Hypergraphs [0.7835894511242797]
  We present a novel framework based in the theory of hypergraphs and topology to understand data from cyber networks. We will demonstrate concrete examples in a large-scale cyber network dataset.
  arXiv  Detail & Related papers  (2023-11-08T00:00:33Z)
• Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management [61.88858330222619]
  We present an approach for predicting trust links between peers in social media. We propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset.
  arXiv  Detail & Related papers  (2021-11-11T19:40:51Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation [5.196831100533835]
  Generative Adversarial Networks (GANs) constitute an extensively researched machine learning sub-field. GANs are typically used to generate or transform synthetic images. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks.
  arXiv  Detail & Related papers  (2021-05-10T08:28:36Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• A Latent Space Model for Multilayer Network Data [0.2538209532048866]
  We propose a Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors. The key feature of the model is a hierarchical prior distribution that allows us to represent the entire system jointly. Our model's capabilities are illustrated using several real-world data sets, taking into account different types of actors, sizes, and relations.
  arXiv  Detail & Related papers  (2021-02-18T00:53:44Z)
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [95.27249880156256]
  We develop and present a unified architecture of network community-finding methods. We introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
  arXiv  Detail & Related papers  (2021-01-03T02:32:45Z)
• A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning [54.55119659523629]
  Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere.
  arXiv  Detail & Related papers  (2020-10-15T14:12:26Z)
• Who2com: Collaborative Perception via Learnable Handshake Communication [34.29310680302486]
  We propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task. Inspired by networking communication protocols, we propose a multi-stage handshake communication mechanism where the neural network can learn to compress relevant information needed for each stage. We show that for the semantic segmentation task, our handshake communication method significantly improves accuracy by approximately 20% over decentralized baselines, and is comparable to centralized ones using a quarter of the bandwidth.
  arXiv  Detail & Related papers  (2020-03-21T04:16:22Z)
• I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance [79.05613148641018]
  We will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance.
  arXiv  Detail & Related papers  (2020-02-05T11:10:44Z)

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.