Spam Detection for Social Media Posts Using Convolutional Neural Networks

In this article, we’ll explore a machine learning approach to detect spam in social media posts using a Convolutional Neural Network (CNN). This method leverages deep learning techniques to identify patterns in text data that are indicative of spam content.

Data Preparation and Preprocessing

Our dataset consists of social media posts labeled as either spam (1) or non-spam (0). Here’s a glimpse of our data:

data = {
    'post': [
        'Congratulations! You have won a lottery!',
        'Check out our new product!',
        'Join our group for more information!',
        'I love spending time with my friends.',
        'Limited time offer! Click now!',
        'What a beautiful day!',
        'Earn money from home, apply now!',
        'Let's catch up soon!'
    ],
    'label': [1, 1, 1, 0, 1, 0, 1, 0]  # 1=spam, 0=ham
}

We start by creating a pandas DataFrame and splitting the data into training and testing sets:

df = pd.DataFrame(data)
X_train, X_test, y_train, y_test = train_test_split(df['post'], df['label'], test_size=0.2, random_state=0)

Text Tokenization and Padding