Detecting fake accounts through Generative Adversarial Network in online social media

• URL: http://arxiv.org/abs/2210.15657v4
• Date: Wed, 20 Dec 2023 08:17:39 GMT
• Title: Detecting fake accounts through Generative Adversarial Network in online social media
• Authors: Jinus Bordbar, Mohammadreza Mohammadrezaie, Saman Ardalan, Mohammad Ebrahim Shiri
• Abstract summary: This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset. Despite the problem's complexity, the method achieves an AUC rate of 80% in classifying and detecting fake accounts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online social media is integral to human life, facilitating messaging, information sharing, and confidential communication while preserving privacy. Platforms like Twitter, Instagram, and Facebook exemplify this phenomenon. However, users face challenges due to network anomalies, often stemming from malicious activities such as identity theft for financial gain or harm. This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset. Despite the problem's complexity, the method achieves an AUC rate of 80\% in classifying and detecting fake accounts. Notably, the study builds on previous research, highlighting advancements and insights into the evolving landscape of anomaly detection in online social networks.

Related papers

• Easy-access online social media metrics can effectively identify misinformation sharing users [41.94295877935867]
  We find that higher tweet frequency is positively associated with low factuality in shared content, while account age is negatively associated with it. Our findings show that relying on these easy-access social network metrics could serve as a low-barrier approach for initial identification of users who are more likely to spread misinformation.
  arXiv  Detail & Related papers  (2024-08-27T16:41:13Z)
• Characteristics and prevalence of fake social media profiles with AI-generated faces [7.444681337745949]
  Recent advancements in generative artificial intelligence (AI) have raised concerns about their potential to create convincing fake social media accounts. We present a systematic analysis of Twitter accounts using human faces generated by Generative Adrial Networks (GANs) for their profile pictures. We show that they are used to spread scams, spam, and amplify coordinated messages, among other inauthentic activities.
  arXiv  Detail & Related papers  (2024-01-05T04:10:46Z)
• Classification of Instagram fake users using supervised machine learning algorithms [0.0]
  This paper proposes an application designed to detect and neutralize such dishonest entities. The user-centric design of the application ensures accessibility for investigative agencies.
  arXiv  Detail & Related papers  (2023-11-21T03:59:14Z)
• Unsupervised detection of coordinated fake-follower campaigns on social media [1.3035246321276739]
  We present a novel unsupervised detection method designed to target a specific category of malicious accounts. Our framework identifies anomalous following patterns among all the followers of a social media account. We find that these detected groups of anomalous followers exhibit consistent behavior across multiple accounts.
  arXiv  Detail & Related papers  (2023-10-31T12:30:29Z)
• User Identity Linkage in Social Media Using Linguistic and Social Interaction Features [11.781485566149994]
  User identity linkage aims to reveal social media accounts likely to belong to the same natural person. This work proposes a machine learning-based detection model, which uses multiple attributes of users' online activity. The models efficacy is demonstrated on two cases on abusive and terrorism-related Twitter content.
  arXiv  Detail & Related papers  (2023-08-22T15:10:38Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
  Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
  arXiv  Detail & Related papers  (2022-12-27T16:08:49Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News [67.53424807783414]
  Social media has greatly enabled people to participate in online activities at an unprecedented rate. This unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.
  arXiv  Detail & Related papers  (2020-04-03T18:26:33Z)
• 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.