Signed Latent Factors for Spamming Activity Detection

• URL: http://arxiv.org/abs/2209.13814v1
• Date: Wed, 28 Sep 2022 03:39:34 GMT
• Title: Signed Latent Factors for Spamming Activity Detection
• Authors: Yuli Liu
• Abstract summary: We propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications are interpreted by the unified signed network. Experiments on real-world datasets of different kinds of Web applications indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities.
• Score: 1.8275108630751844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors, or relational ties. These works have made considerable progress in understanding and filtering spamming campaigns. However, this problem remains far from fully solved. Almost all the proposed features focus on a limited number of observed attributes or explainable phenomena, making it difficult for existing methods to achieve further improvement. To broaden the vision about solving the spam problem and address long-standing challenges (class imbalance and graph incompleteness) in the spam detection area, we propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications in this scenario are interpreted by the unified signed network. Two competitive and highly dissimilar algorithms of latent factors mining (LFM) models are designed based on multi-relational likelihoods estimation (LFM-MRLE) and signed pairwise ranking (LFM-SPR), respectively. We then explore how to apply the mined latent factors to spam detection tasks. Experiments on real-world datasets of different kinds of Web applications (social media and Web forum) indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities. By specifically manipulating experimental data, the effectiveness of our methods in dealing with incomplete and imbalanced challenges is valida

Related papers

• Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo [55.452453947359736]
  We introduce a novel verification method based on Twisted Sequential Monte Carlo (TSMC) We apply TSMC to Large Language Models by estimating the expected future rewards at partial solutions. This approach results in a more straightforward training target that eliminates the need for step-wise human annotations.
  arXiv  Detail & Related papers  (2024-10-02T18:17:54Z)
• LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM [1.8326853076179552]
  We introduce LLMCount, the first system to harness the capabilities of large-language models (LLMs) to enhance crowd detection performance. To assess the system's performance, comprehensive evaluations are conducted under diversified scenarios like hall, meeting room, and cinema.
  arXiv  Detail & Related papers  (2024-09-24T16:09:29Z)
• Online detection and infographic explanation of spam reviews with data drift adaptation [4.278181795494584]
  This paper proposes an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The best results obtained reached up to 87 % spam F-measure.
  arXiv  Detail & Related papers  (2024-06-21T10:35:46Z)
• Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking [48.68044413117397]
  PINOSE trains a probing model on offline self-consistency checking results, thereby circumventing the need for human-annotated data. It examines various aspects of internal states prior to response decoding, contributing to more effective detection of factual inaccuracies.
  arXiv  Detail & Related papers  (2024-04-10T05:00:35Z)
• Evaluating the Performance of ChatGPT for Spam Email Detection [9.585304538597414]
  This study attempts to evaluate ChatGPT's capabilities for spam identification in both English and Chinese email datasets. We employ ChatGPT for spam email detection using in-context learning, which requires a prompt instruction and a few demonstrations. We also investigate how the number of demonstrations in the prompt affects the performance of ChatGPT.
  arXiv  Detail & Related papers  (2024-02-23T04:52:08Z)
• Amortizing intractable inference in large language models [56.92471123778389]
  We use amortized Bayesian inference to sample from intractable posterior distributions. We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
  arXiv  Detail & Related papers  (2023-10-06T16:36:08Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Modeling User Behavior With Interaction Networks for Spam Detection [4.795582035438343]
  Spam is a serious problem plaguing web-scale digital platforms. We propose SEINE, a spam detection model over a novel graph framework. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers.
  arXiv  Detail & Related papers  (2022-07-21T21:34:56Z)
• Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text [219.15486286590016]
  This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.
  arXiv  Detail & Related papers  (2021-10-10T17:19:37Z)
• Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection [14.203689072168672]
  Multi-level Dependency Model (MDM) is able to exploit user's long-term dependency hidden in their relational sequences. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM.
  arXiv  Detail & Related papers  (2020-09-14T07:11:17Z)
• Robust Spammer Detection by Nash Reinforcement Learning [64.80986064630025]
  We develop a minimax game where the spammers and spam detectors compete with each other on their practical goals. We show that an optimization algorithm can reliably find an equilibrial detector that can robustly prevent spammers with any mixed spamming strategies from attaining their practical goal.
  arXiv  Detail & Related papers  (2020-06-10T21:18:07Z)

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.