Independent research paper · 2024
A Minimal Approach to Fake News Detection
A six-month research project testing whether five article-level features could distinguish fake news from real news.
Outcome
The five-feature XGBoost model reached 71% test accuracy, and the paper was published in 2024.
- Role
- Researcher and author
- Year
- 2024
- Team
- Independent
- Tools
- Machine learning, Natural language processing
6 months
spent researching and writing the paper
01
A deliberately small feature set
Over six months, I studied how other researchers had applied machine learning to fake-news detection. I wanted to find out whether a useful model could work with as few textual features as possible.
The resulting paper reviews established approaches and presents my own minimal method. The five-feature XGBoost model reached 71% test accuracy.
02
Learning through publication
Researching, testing and writing the paper gave me practical experience with natural-language processing and machine learning.
Next
What came next.
The paper is published and available on ResearchGate.
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