# Social Fraud Detection Review: Methods, Challenges and Analysis

arXiv: https://arxiv.org/abs/2111.05645 · HTML: https://mlsanity.com/paper/2111.05645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 115 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection](https://mlsanity.com/paper/2005.10150/) (2020)
- [Automatic Detection of Cyberbullying in Social Media Text](https://mlsanity.com/paper/1801.05617/) (2018)
- [Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations](https://mlsanity.com/paper/1805.03322/) (2018)
- [Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking](https://mlsanity.com/paper/2011.13253/) (2020)
- [Automated Crowdturfing Attacks and Defenses in Online Review Systems](https://mlsanity.com/paper/1708.08151/) (2017)
- [Detecting Deceptive Reviews using Generative Adversarial Networks](https://mlsanity.com/paper/1805.10364/) (2018)
- [A framework for fake review detection in online consumer electronics retailers](https://mlsanity.com/paper/1903.12452/) (2019)
- [HoloScope: Topology-and-Spike Aware Fraud Detection](https://mlsanity.com/paper/1705.02505/) (2017)
- [Non-native children speech recognition through transfer learning](https://mlsanity.com/paper/1809.09658/) (2018)
- [Detecting Singleton Review Spammers Using Semantic Similarity](https://mlsanity.com/paper/1609.02727/) (2016)
- [Opinion Fraud Detection via Neural Autoencoder Decision Forest](https://mlsanity.com/paper/1805.03379/) (2018)
- [DFraud3- Multi-Component Fraud Detection freeof Cold-start](https://mlsanity.com/paper/2006.05718/) (2020)
- [Multi-layer graph analysis for dynamic social networks](https://mlsanity.com/paper/1309.5124/) (2013)

## Abstract (arXiv)

Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.