An effective spam filter powered by neural networks: a solution for clean chats and user safety.

Context and Goal

Learn how we built a powerful spam filter using artificial intelligence. The technology is trained on hundreds of thousands of spam messages to protect chats from ads, fraud, and unwanted content.

Success Criteria

Business metrics and operational KPI.
Data readiness and integration quality.
Security and compliance requirements.

Tasks

What needed to be solved and why it mattered for the business.

Data Collection and Segmentation

In the first phase, we need to gather a large volume of messages from various sources, including chats, groups, and public channels. The data is then segmented by key features to create a high-quality base for subsequent labeling and training.

Data labeling.

After collecting the data, it's important to label it by categorizing each message. This allows you to separate spam, fraudulent messages, advertisements, and legitimate content. Labeling is a crucial step in creating an accurate model.

Training a neural network

Training a neural network is done on labeled data. The network needs to learn to identify spam, detect fraudulent schemes, and distinguish unwanted messages from legitimate ones with high accuracy.

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After training, the neural network is tested on new data that it hasn't seen before. This helps identify weaknesses in the model and allows for necessary adjustments to improve filtering accuracy.

Telegram Bot Integration

The final stage involves integrating the trained and tested neural network into a Telegram bot system. The bot gains the ability to filter messages in real time, automatically blocking spam and unwanted content.

Solution Milestones

How we built delivery: from hypothesis to production.

1

Requirements research and analysis.

Conducting an analysis of current tasks and goals, and identifying the types of messages that need to be filtered. Creating a technical specification based on client requirements.

2

Data Collection

A service for collecting large volumes of messages from chats and groups, including via the Telegram API, while adhering to all legal and ethical guidelines.

3

Data Cleaning and Segmentation

Removing duplicate or irrelevant messages, grouping data by categories, and preparing it for labeling.

4

Data labeling.

Manually classifying messages and using automated tools to identify spam, promotional content, and fraudulent schemes.

5

Training a neural network

Using PyTorch to build a neural network. Training on labeled data using architectures like Transformer for effective message classification.

6

Model testing.

Testing a trained model on a validation dataset. Analyzing metrics such as accuracy, recall, and F1-score to evaluate the model's performance.

7

Integrating the model into a Telegram bot

Developing functionality for model interaction with a bot, and setting up automatic message filtering. Implementing a logging system for monitoring and improvements.

8

Optimization and maintenance

Regular system performance checks. Additional model training on new data and parameter updates to improve efficiency.

Results

Business impact validated by measurable outcomes.

Reducing the burden on administrators.

Now chat admins don't waste time manually deleting spam and moderating. An AI takes on the task of keeping things clean, allowing administrators to focus on more important tasks.

High accuracy in spam detection.

Our neural network is trained on a vast dataset, enabling it to identify spam, promotional messages, and fraudulent schemes with high accuracy. The average classification accuracy is over 95%.

Instant unwanted message removal.

The neural network quickly processes incoming messages, identifies their category, and instantly removes spam, making chats cleaner and more user-friendly.

Boosting user trust

Thanks to automated moderation, chats are safer, which increases user loyalty and engagement. People are no longer worried about scams or unwanted ads.

Saving time and resources

Integrating a spam filter with the Telegram bot has reduced manual moderation costs and decreased user complaints about unwanted content.

Flexibility and scalability of the solution.

The model is designed for easy adaptation to other messaging platforms and projects, making it versatile for a wide range of applications.

Technology

Tools and engineering stack used in delivery.

Python

The primary programming language used for developing the solution is Python. It offers flexibility and high performance when working with large datasets and integrating neural networks.

PyTorch

A deep learning library that enabled us to efficiently train and test neural networks. PyTorch was chosen for its speed and ease of use with neural networks.

Transformer

A transformer model was used to process text and analyze context. Transformers significantly improved the neural network's ability to understand and classify messages with high accuracy.

Aiogram

A library for interacting with the Telegram Bot API. Aiogram facilitated the integration of a neural network with Telegram, enabling real-time chat moderation.

FAQ

Answers to common questions about this case.

What is a neural network spam filter, and how does it work?

A neural network spam filter is a system that uses artificial intelligence to automatically filter spam in chats and messaging apps. Our neural network has been trained on millions of messages, enabling it to accurately identify and remove spam, promotional, and fraudulent messages, reducing the workload for chat administrators.

What technologies are used to create a spam filter with a neural network?

We use Python, PyTorch, a Transformer model, and the Aiogram library to create an effective spam filter. These technologies provide high accuracy in message classification and fast integration with chatbots on Telegram.

How does a neural network identify spam?

The neural network is trained on large amounts of data and uses machine learning algorithms to identify spam indicators, such as frequently used phrases, links to fraudulent websites, and keywords characteristic of advertising and phishing.

How does a spam filter with a neural network help improve the work of chat administrators?

A spam filter with a neural network significantly reduces the workload for chat administrators by automatically removing spam messages. This allows administrators to focus on more important tasks without spending time constantly reviewing messages.

What are the advantages of using a neural network for spam filtering in chats?

Key benefits include: - High accuracy: The neural network is trained to recognize various types of spam with minimal errors. - Automation: Spam messages are removed instantly, without human intervention. - Continuous learning: The neural network continuously learns and improves its effectiveness based on new data.

Can a spam filter using a neural network be integrated with other messengers besides Telegram?

Yes, our system can be adapted and integrated with other messengers and platforms using appropriate APIs, enabling automatic spam filtering in any chat application.

How is a neural network trained to filter spam?

We train a neural network using large datasets containing both legitimate messages and spam. Each type of message is labeled and used to train the model, allowing the neural network to recognize patterns characteristic of spam.

How long does it take to train a neural network for spam filtering?

Training a neural network can take anywhere from a few days to several weeks, depending on the amount of data and the complexity of the tasks. We are constantly updating and improving the model to keep it current.

How can I improve the spam filter in my group or chat?

To improve spam filter performance, it's recommended to regularly update the database with new spam examples and customize the filtering based on the specifics of your chat or community.