# Signed Latent Factors for Spamming Activity Detection

arXiv: https://arxiv.org/abs/2209.13814 · HTML: https://mlsanity.com/paper/2209.13814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Spam Review Detection with Graph Convolutional Networks](https://mlsanity.com/paper/1908.10679/) (2019)
- [A Survey of Signed Network Mining in Social Media](https://mlsanity.com/paper/1511.07569/) (2015)
- [SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection](https://mlsanity.com/paper/1312.5035/) (2013)
- [Fact or Factitious? Contextualized Opinion Spam Detection](https://mlsanity.com/paper/2010.15296/) (2020)
- [Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network](https://mlsanity.com/paper/2202.06096/) (2022)
- [SINE: Scalable Incomplete Network Embedding](https://mlsanity.com/paper/1810.06768/) (2018)
- [Detecting Singleton Review Spammers Using Semantic Similarity](https://mlsanity.com/paper/1609.02727/) (2016)
- [A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System](https://mlsanity.com/paper/2111.10280/) (2021)
- [Modeling User Behavior With Interaction Networks for Spam Detection](https://mlsanity.com/paper/2207.10767/) (2022)
- [What They Do in Shadows: Twitter Underground Follower Market](https://mlsanity.com/paper/1411.4299/) (2014)
- [FLOCK: Combating Astroturfing on Livestreaming Platforms](https://mlsanity.com/paper/1610.01096/) (2016)
- [Modeling Review Spam Using Temporal Patterns and Co-bursting Behaviors](https://mlsanity.com/paper/1611.06625/) (2016)
- [Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings](https://mlsanity.com/paper/1710.05613/) (2017)
- [Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms](https://mlsanity.com/paper/2205.13422/) (2022)

## Abstract (arXiv)

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 studies 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 validated.