Fake News Detection System Based on NLP and Logistic Regression: A Lightweight Solution Implemented with Streamlit

In the era of information explosion, fake news has become a prevalent social problem. This project provides a practical machine learning tool that combines natural language processing (NLP) technology and logistic regression models. It builds a lightweight web application via Streamlit to realize authenticity classification of news texts and visualization of prediction probabilities, helping users quickly identify potential false information.

Fake news spreads faster than real information (the 'lie treadmill effect'). Traditional manual review struggles to handle massive content, and automated detection faces challenges such as semantic understanding, sarcasm recognition, and context dependency. The entry point of this project is to build a lightweight, interpretable, and easy-to-deploy detection prototype.

Technical Selection: Advantages of Logistic Regression

Compared to complex deep learning models, logistic regression has fast training speed, strong interpretability (coefficients map to feature importance), stability on small to medium datasets, and low resource consumption for inference, making it suitable for scenarios where the basis for judgment needs to be explained.

NLP Pipeline Design

It includes text preprocessing (case conversion, punctuation removal, stopword filtering) and feature extraction (bag-of-words/TF-IDF vectorization) to capture surface-level linguistic features (exaggerated vocabulary, emotional polarity, etc.).

Streamlit Interactive Interface

A concise interface built with Streamlit: users input text, and the system returns classification results and confidence visualization in real time (charts show the probability distribution of real/fake news), helping to understand decision uncertainty.

Training data usually includes news titles, body text, and manually labeled authenticity tags. The logistic regression model acquires feature weight patterns through supervised learning. Note the challenge of concept drift: fake news writing strategies evolve over time, so the model needs regular updates to maintain effectiveness. (Note: The project repository does not explicitly specify the source of training data.)

Application Scenarios

Suitable for individuals to quickly verify suspicious news, media literacy teaching in educational institutions, and prototype verification of complex systems.

Limitations

• Bag-of-words-based features struggle to capture deep semantics and cross-sentence reasoning, and may fail for carefully designed misleading content;
• Model judgments are affected by biases in training data, reflecting the subjective standards of annotators;
• Output should be used as an auxiliary reference rather than an authoritative ruling.

Future improvement directions: Introduce pre-trained language models like BERT to enhance semantic understanding, integrate source credibility assessment, add multilingual support, and establish a user feedback mechanism for continuous learning. The current version serves as a good foundation for proof of concept and teaching demonstrations.