Summary of A Systematic Review Of Machine Learning Approaches For Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases, by Yunchong Liu et al.
A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases
by Yunchong Liu, Xiaorui Shen, Yeyubei Zhang, Zhongyan Wang, Yexin Tian, Jianglai Dai, Yuchen Cao
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A systematic review evaluates 36 machine learning (ML) and deep learning (DL) studies that detect fake news, spam, and fake accounts on social media. The study identifies biases in the ML lifecycle, including selection bias due to non-representative sampling, class imbalance, inadequate linguistic preprocessing, and inconsistent hyperparameter tuning. Strong potential is shown by models like Support Vector Machines (SVM), Random Forests, and Long Short-Term Memory (LSTM) networks, but overreliance on accuracy in imbalanced data settings is a common flaw. The review highlights the need for improved data preprocessing, consistent hyperparameter tuning, and the use of metrics like precision, recall, F1 score, and AUROC to develop more reliable ML/DL models for detecting deceptive content, reducing misinformation on social media. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning can help stop fake news from spreading on social media. It studies 36 different ways that machine learning models have been used to spot fake news, spam, and fake accounts. The study finds some problems with these approaches, like using the wrong methods or not preparing the data properly. Despite this, some models show promise in detecting fake content. To make these models better, the paper suggests we need to improve how we prepare the data, use more consistent methods, and focus on different measures of success. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Hyperparameter » Lstm » Machine learning » Precision » Recall
