🤖 SoMe: A Realistic Benchmark for LLM-based Social Media Agents

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📋 Overview

SoMe is a comprehensive benchmark designed to evaluate the capabilities of Large Language Model (LLM)-based agents in realistic social media scenarios. This benchmark provides a standardized framework for testing and comparing social media agents across multiple dimensions of performance.

SoMe comprises a diverse collection of:

• 8 social media agent tasks
• 9,164,284 posts from various social media platforms
• 6,591 user profiles with rich behavioral data
• 25,686 reports from external websites
• 17,869 meticulously annotated task queries

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📰 News

• [2025.11] 🎉 Our paper is accepted by AAAI 2026!

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✨ Features

SoMe benchmark evaluates social media agents across 8 key tasks, covering diverse aspects of social media intelligence:

Task Category	Task Name	Description
Post-centered	🚨 Realtime Event Detection (RED)	Identify and track emerging events in real-time
Post-centered	📊 Streaming Event Summary (SES)	Summarize ongoing events from streaming data
Post-centered	🚫 Misinformation Detection (MID)	Identify and flag potentially false or misleading information
User-centered	🎯 User Behavior Prediction (UBP)	Predict user interactions with social media content
User-centered	😊 User Emotion Analysis (UEA)	Analyze user emotions towards social media content
User-centered	💬 User Comment Simulation (UCS)	Simulate realistic user comments
Comprehensive	📱 Media Content Recommendation (MCR)	Recommend relevant media content based on user interests
Comprehensive	❓ Social Media Question-Answering (SMQ)	Accurately answer questions about social media content

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📈 Dataset Statistics

The SoMe benchmark includes comprehensive datasets for each task, with the following statistics:

Task	# Query	# Data	Data Type
🚨 Real-time Event Detection	568	476,611	Posts
📊 Streaming Event Summary	154	7,898,959	Posts
🚫 Misinformation Detection	1,451	27,137	Posts & Knowledge
🎯 User Behavior Prediction	3,000	840,200	Posts & Users
😊 User Emotion Analysis	2,696	840,200	Posts & Users
💬 User Comment Simulation	4,000	840,200	Posts & Users
📱 Media Content Recommendation	4,000	840,200	Posts & Users
❓ Social Media Question-Answering	2,000	8,651,759	Posts & Users
Total	17,869	9,242,907	All

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📁 Project Structure

Social-Media-Agent/
├── 🤖 agent.py                    # Main social media agent implementation
├── 🔧 qwen_agent/                 # Qwen-Agent library
├── 📋 tasks/                      # Task-specific modules
│   ├── 📱 media_content_recommend/
│   ├── 🚫 misinformation_detection/
│   ├── 🚨 realtime_event_detection/
│   ├── ❓ social_media_question_answering/
│   ├── 📊 streaming_event_summary/
│   ├── 💬 user_comment_simulation/
│   ├── 😊 user_emotion_analysis/
│   └── 🎯 user_behavior_prediction/
├── 🛠️ tools/                      # Tools for social media analysis
├── 🧪 test_*.py                   # Test scripts for each task
├── 📊 eval_scripts/               # Evaluation scripts for scoring
├── 📂 results/                    # Directory for storing results
├── 📊 datasets/                   # Dataset directory
└── 💾 database/                   # Database directory

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🚀 Installation

Prerequisites

• Python 3.12+ installed on your system
• Git installed for repository cloning
• Sufficient disk space for data (recommended: 50GB+)

Installation Steps

1. 📥 Clone the repository

   git clone https://github.com/LivXue/SoMe.git
   cd SoMe
   

2. 📦 Install dependencies

   pip install -r requirements.txt
   

3. 📥 Download test data

   • Hugging Face Dataset: Download Link
   • Google Drive: Download Link
   • Baidu Disk: Download Link (Password: SoMe)

   After downloading, unzip the data into the database directory.

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💻 Usage

🏃‍♂️ Running Individual Tasks

Each task can be evaluated using its corresponding test script:

# 🚨 Realtime Event Detection
python test_realtime_event_detection.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

# 📊 Streaming Event Summary
python test_streaming_event_summary.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

# 🚫 Misinformation Detection
python test_misinformation_detection.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

# 🎯 User Behavior Prediction
python test_user_behavior_prediction.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

# 😊 User Emotion Analysis
python test_user_emotion_analysis.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

# 💬 User Comment Simulation
python test_user_comment_simulation.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

# 📱 Media Content Recommendation
python test_media_content_recommend.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

# ❓ Social Media Question Answering
python test_social_media_question_answering.py --model MODEL_NAME --base_url MODEL_SERVER_URL --api_key API_KEY

⚙️ Command Line Arguments

Argument	Description	Example
--model	The model name to use	"deepseek-chat"
--base_url	The base URL for the model server	"https://api.deepseek.com"
--api_key	The API key for the model server	Your actual API key
--output_path	Output path for results	"results/my_experiment"

📊 Evaluation

After running the test scripts, evaluate the results using the provided evaluation scripts:

# Option 1: For tasks with LLM-based answer extraction
python eval_scripts/[TASK]_extraction.py
python eval_scripts/[TASK]_compute_score.py

# Option 2: For tasks with LLM-as-judge scoring
python eval_scripts/[TASK]_scoring.py
python eval_scripts/[TASK]_compute_score.py

Note: The LLM settings for evaluation are configured in eval_scripts/settings.json

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🧠 Model Support

The benchmark supports various LLM models through OpenAI-compatible API endpoints:

• 🧩 Qwen series models (Qwen2.5, Qwen3, etc.)
• 🔌 OpenAI models (GPT-4, GPT-5, etc.)
• 🌐 Third-party models with OpenAI-compatible APIs (DeepSeek, Claude, etc.)
• 📦 Local models served with OpenAI-compatible wrappers (vLLM, Ollama, etc.)

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📚 Citation

If you use this benchmark in your research, please cite our paper:

@inproceedings{some2026,
  title={SoMe: A Realistic Benchmark for LLM-based Social Media Agents},
  author={Dizhan Xue and Jing Cui and Shengsheng Qian and Chuanrui Hu and Changsheng Xu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2026}
}

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🤝 Contributing

We welcome contributions to improve the benchmark! Here's how you can help:

1. 🐛 Report bugs by opening issues with detailed descriptions
2. 💡 Suggest features for new tasks or improvements
3. 🔧 Submit code via pull requests for bug fixes or enhancements
4. 📊 Add datasets to expand the benchmark coverage
5. 📝 Improve documentation for better usability

Please see our Contributing Guidelines for more details.

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📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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🙏 Acknowledgments

We would like to express our gratitude to:

• The Qwen team for their excellent Qwen-Agent framework, which forms the foundation of this benchmark
• All contributors who have helped develop and improve SoMe
• The social media platforms and data providers that make this research possible
• The AAAI 2026 reviewers for their valuable feedback

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📞 Contact

For questions or inquiries about the benchmark, please contact:

• Dizhan Xue: xuedizhan17@mails.ucas.ac.cn

Visit our GitHub repository for the latest updates and discussions.