IMMENSE: Inductive Multi-perspective User Classification in Social Networks

• URL: http://arxiv.org/abs/2608.05259v1
• Date: Wed, 05 Aug 2026 17:00:26 GMT
• Title: IMMENSE: Inductive Multi-perspective User Classification in Social Networks
• Abstract summary: IMMENSE is a machine learning-based method for detecting malicious social network users.<n>Our approach integrates three perspectives: the semantics of the users' published content, their social relationships and their spatial information.<n>Experiments carried out on a real-world Twitter/X dataset showed the superiority of IMMENSE against five state of the art competitors.
• Score: 5.183308052689481
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content. Young users, in particular, are vulnerable to exposure to such content, which can have harmful psychological and social repercussions. Given the massive scale of today's social networks, in terms of both published content and number of users, there is an urgent need for effective systems to aid Law Enforcement Agencies (LEAs) in identifying and addressing users that disseminate malicious content. In this work we introduce IMMENSE, a machine learning-based method for detecting malicious social network users. Our approach adopts a hybrid classification strategy that integrates three perspectives: the semantics of the users' published content, their social relationships and their spatial information. Such contextual perspectives potentially enhance classification performance beyond text-only analysis. Importantly, IMMENSE employs an inductive learning approach, enabling it to classify previously unseen users or entire new networks without the need for costly and time-consuming model retraining procedures. Experiments carried out on a real-world Twitter/X dataset showed the superiority of IMMENSE against five state of the art competitors, confirming the benefits of its hybrid approach for effective deployment in social network monitoring systems.

Related papers

• Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based Machine Learning Framework [5.635300481123079]
  Social media platforms have given rise to negative behaviors in the online community, the so-called cyberbullying.<n>We propose an innovative and real-time solution for cyberbullying detection using stream-based Machine Learning (ML) models and Large Language Models (LLMS)<n>Results on experimental data report promising performance close to 90 % in all evaluation metrics and surpassing those obtained by competing works in the literature.
  arXiv  Detail & Related papers  (2025-04-07T15:57:37Z)
• 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)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Domain-based user embedding for competing events on social media [4.984601297028257]
  We develop a method for embedding users based on a URL domain co-occurrence network.<n>This approach effectively represents social media users involved in competing events such as political campaigns and public health crises.
  arXiv  Detail & Related papers  (2023-08-28T18:01:14Z)
• 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)
• Federated Social Recommendation with Graph Neural Network [69.36135187771929]
  We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
  arXiv  Detail & Related papers  (2021-11-21T09:41:39Z)
• 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)
• Towards A Multi-agent System for Online Hate Speech Detection [11.843799418046666]
  This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook. We introduce a novel framework employing deep learning techniques to coordinate the channels of textual and im-age processing.
  arXiv  Detail & Related papers  (2021-05-03T19:06:42Z)
• 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)
• Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics [43.98568073610101]
  We use a social media model to quantify the impacts of several adversarial manipulation tactics on the quality of content. We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
  arXiv  Detail & Related papers  (2019-07-13T21:12:08Z)

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.