📰🔍 FakeLense: Leveraging a Hybrid BERT-GPT Model for Robust Detection of Disinformation

🎓 Introduction

Fake news refers to intentionally fabricated information spread to mislead, manipulate, or gain attention for political, economic, or other malicious purposes. The rapid proliferation of fake news, fueled by advancements in social media (SNS) and artificial intelligence (AI), has led to increasingly severe negative impacts on society. With the development of generative AI technologies, the risk of widespread distribution of fake news that is difficult to distinguish from genuine information has amplified. Moreover, fake news creators are becoming more sophisticated, concealing their identities and engaging in activities exacerbating social division and polarization.

In response to the significant security threats posed by false and manipulated information, governments worldwide are establishing new organizations dedicated to combating these issues. This project, FakeLense, aims to contribute to these efforts by developing an advanced tool for detecting fake news using cutting-edge natural language processing (NLP) technology.

📑 Project Overview

FakeLense is an NLP-powered tool specifically designed to automatically detect and prevent the spread of fake news and disinformation. By leveraging a hybrid detection system that combines the text comprehension capabilities of BERT (Bidirectional Encoder Representations from Transformers) and the generative abilities of GPT (Generative Pre-trained Transformer) Large Language Models, FakeLense aims to achieve a high accuracy in identifying and counteracting disinformation. To train these models effectively, a dataset of 63,678 real and fake news texts was crawled and utilized, ensuring robust performance in distinguishing between genuine content and disinformation.

Key Features

• Hybrid Model Approach: FakeLense combines BERT-based and GPT-based models to enhance detection accuracy.
• Focus on Disinformation: The project targets the detection and prevention of disinformation—factually incorrect and maliciously intended content.
• Real-time Detection: FakeLense can be integrated into content platforms to monitor and flag potential fake news in real-time, preventing dissemination.
• Mitigating Social Harm: By effectively blocking fake news, FakeLense aims to reduce unnecessary social conflicts, prevent the polarization of public opinion, and save national resources from being wasted on dealing with the consequences of misinformation.

What is Fake News?

Fake news can be categorized into three main types:

1. DisInformation: Information that is factually incorrect and maliciously intended (false O, malicious O).
2. Exaggerated Information: Information that is factually correct but exaggerated with malicious intent (false X, malicious O).
3. MisInformation: Information that is factually incorrect but not maliciously intended (false O, malicious X).

FakeLense focuses on detecting and blocking Disinformation—the most harmful type of fake news that misleads the public with incorrect data and malicious motives.

🛠️ Usage

Before you begin, ensure that you have Python 3.7 or higher installed. Install the required dependencies with the following command:

  pip install torch transformers scikit-learn pandas datasets

These dependencies include essential libraries for machine learning, natural language processing, and data handling.

STEP 0. Clone the Repository

Start by cloning the repository to your local machine:

  git clone https://github.com/Navy10021/FakeLense.git
  cd FakeLense

STEP 1. Prepare the Dataset

In this project, 63,678 real and fake news texts were crawled to train the FakeLense model. To prepare the dataset, run the 'preprocessing.py' script. This script will automatically preprocess the text, label it, and split it into training and testing datasets with an 8:2 ratio.

Run the following command:

  python preprocessing.py

After running this script, you should have two files in the ./data/ folder:

• train.csv: Training data
• test.csv: Testing data

Each CSV file will have the following columns:

• text: The text of the news article.
• title : The title of the news article
• target: The label (0 for real, 1 for fake).

STEP 2. Training

To train both the BERT and GPT models, run the 'train.py' script:

  python train.py

This script will:

• Fine-tune the BERT-based model and save it in ./model/bert_lense.
• Fine-tune the GPT-based model and save it in ./model/gpt_lense.

STEP 3. Detection

After training, you can perform fake news detection by running the 'detect.py' script:

  python detect.py

You can modify the 'test_cases' list in the script with your own examples for testing.

  test_cases = [
   "In the wake of the recent election, residents of Amherst gathered at the local common...",
   "In a shocking twist, FBI Special Agent David Raynor, who was reportedly investigating a connection between Hillary Clinton...",
   ...
]

An example output is as follows:

News 1: Real News Detected.
News 2: Fake News Detected.
News 3: Fake News Detected.
News 4: Real News Detected.

🏋️‍♂️ Training Phase

BERTLense: Train BERT-Based Model

BERT-based models can be fine-tuned using the 'train_bert' function on pre-trained BERT-based LLMs. Here, you can build BERTLense by applying various BERT-based models. The default for LLMs is 'roberta-base'.

  bert_trainer, bert_lense, bert_tokenizer = train_bert('microsoft/deberta-base', train_texts, train_labels, test_texts, test_labels)

GPTLense: Train GPT-Based Model

GPT-based models can be fine-tuned using the 'train_gpt' function on pre-trained GPT-based LLMs. This function allows you to build GPTLense by applying various GPT-based models. The default for LLMs is 'gpt2'.

  gpt_trainer, gpt_lense, gpt_tokenizer = train_gpt('EleutherAI/gpt-neo-125M', train_texts, test_texts)

Both trained models will be saved in the ./model/ directory.

🕵️‍♂️ Detection Phase

The main feature of this code is its implementation, which focuses on enhancing the accuracy of fake news detection by combining the strengths of BERT and GPT. BERT excels at text classification, while GPT provides an additional verification step through its text generation capabilities. Specifically, the FakeLense function synthesizes the results of both models: it identifies fake news if BERT classifies the text as such or if the similarity between the generated text by GPT and the original text is low. This process is used as a strategy to improve the accuracy of fake news detection.

  def FakeLense(text, bert_model, bert_tokenizer, gpt_model, gpt_tokenizer, similarity_threshold=0.8):
   ...

📈 Fake News Detection Performance Evaluation Results

The experimental results demonstrated a high detection accuracy of over 98%, proving the tool's effectiveness in identifying fake news. FakeLense is expected to serve as an innovative "cognitive warfare" tool, capable of addressing misinformation across various channels and contributing to national interests.

👨‍💻 Contributors

• Seoul National University Graduate School of Data Science (SNU GSDS)
• Under the guidance of Navy Lee

📜 License

This project is licensed under the MIT License. See the LICENSE file for more details.