README.md

metadata

title: Bot Detection
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: streamlit
app_file: app.py
pinned: false

Social Media Bot Detection

This project focuses on detecting automated (bot) accounts using only user metadata and behavioral features, without relying on text or content analysis.

The goal is to build a robust and lightweight bot detection system that is less sensitive to content manipulation and language changes.

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What this project does

• Uses user-level metadata and behavioral signals as input
• Performs feature engineering to capture activity patterns
• Trains supervised machine learning models to classify accounts as bot or genuine
• Supports an API-driven setup for frontend or downstream integration

This version intentionally avoids text-based features.

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Why metadata-only detection?

Text-based bot detection can break when:

• Bots generate human-like text
• Language or topics change frequently

Metadata and behavior:

• Are harder to fake consistently
• Capture long-term patterns
• Generalize better across platforms

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Approach (high level)

1. Collect user metadata
2. Clean and preprocess the data
3. Engineer behavioral features
4. Train supervised ML models
5. Evaluate using standard classification metrics
6. Serve predictions via an API

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Model & Code

• Training and inference code are included in this repository
• Model artifacts are not stored here due to size constraints

📦 Trained model weights are hosted on Hugging Face:
👉 https://huggingface.co/spaces/ASHUT0SH-SiNGH/BotDetection

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Notes

• Focuses on pipeline design and modeling logic
• Frontend components are minimal and not the core focus
• Designed to be extended with additional metadata features

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Status

• Model trained and evaluated
• API-based integration supported
• Open to further improvements

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference