# A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases

arXiv: https://arxiv.org/abs/2410.20293 · HTML: https://mlsanity.com/paper/2410.20293/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 71 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Machine Learning vs Deep Learning: The Generalization Problem](https://mlsanity.com/paper/2403.01621/) (2024)
- [GraphSnapShot: Caching Local Structure for Fast Graph Learning](https://mlsanity.com/paper/2406.17918/) (2024)
- [Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges](https://mlsanity.com/paper/2410.16204/) (2024)

## Built on by (most cited first, 4 of 4)

- [Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework](https://mlsanity.com/paper/2508.03092/) (2025)
- [Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection](https://mlsanity.com/paper/2502.04342/) (2025)
- [Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media](https://mlsanity.com/paper/2503.01082/) (2025)
- [CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Media](https://mlsanity.com/paper/2506.05107/) (2025)

## Abstract (arXiv)

Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evaluates 36 studies that apply machine learning (ML) and deep learning (DL) models to detect fake news, spam, and fake accounts on social media. Using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), the review identified key biases across the ML lifecycle: selection bias due to non-representative sampling, inadequate handling of class imbalance, insufficient linguistic preprocessing (e.g., negations), and inconsistent hyperparameter tuning. Although models such as Support Vector Machines (SVM), Random Forests, and Long Short-Term Memory (LSTM) networks showed strong potential, over-reliance on accuracy as an evaluation metric in imbalanced data settings was a common flaw. The review highlights the need for improved data preprocessing (e.g., resampling techniques), consistent hyperparameter tuning, and the use of appropriate metrics like precision, recall, F1 score, and AUROC. Addressing these limitations can lead to more reliable and generalizable ML/DL models for detecting deceptive content, ultimately contributing to the reduction of misinformation on social media.