## 3 Proposed Methodology
### Data Acquisition and Pre-Processing  
A reliable and labeled dataset is crucial to developing an effective fake news classifier. In this study, we utilized the \"Fake News\" dataset ([[https://www.kaggle.com/c/fake-news/data](https://www.kaggle.com/c/fake-news/data)]([https://www.kaggle.com/c/fake-news/data](https://www.kaggle.com/c/fake-news/data)) accessed on 15 September 2023), which comprises a collection of news articles labeled as either \"reliable\" (real news) or \"unreliable\" (fake news). The dataset contains 87,500 articles, distributed into 70,000 and 17,500 classes for reliable and unreliable data, respectively. The dataset provides essential features, such as the article title, text content, publication date, source, and author, enabling us to leverage multiple aspects for classification. The dataset comprises a diverse range of news articles obtained from various sources. Several pre-processing steps were applied to prepare the data for training the fake news classifier. These steps aimed to clean and transform the raw text data into a format suitable for ML algorithms.  
The utilization of specific pre-processing techniques in this study is motivated by established best practices and prior research in the NLP and fake news detection. The application of these pre-processing techniques is crucial in our pursuit of developing an effective fake news classifier. In the era of information overload and the rapid dissemination of news through digital platforms, the quality of the data we feed into our models is paramount. By carefully preparing and cleaning the text data, we aim to reduce noise, extract meaningful information, and enhance the discriminative power of our classifier. These techniques are not merely procedural but are essential for uncovering the underlying patterns and distinguishing characteristics that differentiate fake news from real news. The following pre-processing techniques were employed:  
Stop Word Removal: Stop word removal is a crucial step in fake news detection, eliminating common, insignificant words like \"the\" and \"is\" to focus on meaningful content.  
Figure 1: Proposed methodology for detecting fake news.  
This process reduces noise, improves efficiency, and helps identify relevant keywords and phrases.  
Word Tokenization: Word tokenization involves splitting the text into individual words or tokens. We utilized a tokenization algorithm to break down the text content of each article into a sequence of tokens, facilitating further analysis at the word level.  
URL Removal: Many news articles contain URLs or web links that may not contribute to the classification task. Therefore, we removed the URLs from the text data to focus solely on the textual content of the articles.  
Stemming: Stemming is a technique that reduces words to their base or root form, removing suffixes or prefixes. This process helps reduce the feature space's dimensionality and captures the words' core meaning. We applied a stemming algorithm to transform words into their base form, thus aiding the classification process.  
By implementing these pre-processing steps, we aimed to enhance the quality and relevance of the text data, thus improving the performance of the fake news classifier. The pre-processed dataset was then ready for the subsequent feature engineering, model training, and evaluation stages.