fourbic/disarm-election-watch-dataset

DISARM Election Watch Dataset

Dataset Description

This dataset contains 6,019 examples of election-related content from multiple Nigerian platforms, annotated with DISARM Framework classifications and meta-narrative analysis. The dataset is designed for training models to detect and classify disinformation, misinformation, and coordinated influence operations targeting electoral processes.

Dataset Summary

• Total Examples: 6,019
• Language: English
• Format: JSON (Alpaca format) and JSONL (MLX-LM format)
• License: MIT
• Curated by: DISARM Election Watch Project

Supported Tasks

• Text Classification: DISARM Framework technique classification
• Text Generation: Structured output generation for content analysis
• Sequence Modeling: Meta-narrative pattern recognition

Dataset Structure

Original Alpaca Format (unified_training_dataset.json)

Each example contains:

{
  "instruction": "Classify the following content according to DISARM Framework techniques and meta-narratives:",
  "input_text": "Content to analyze...",
  "output": "Structured classification result..."
}

MLX-LM Text Format (train.jsonl, valid.jsonl, test.jsonl)

Each line contains a JSON object with:

{
  "text": "Complete instruction, input, and response text..."
}

Data Sources

Platform	Examples	Percentage
Telegram	3,632	60.3%
X/Twitter	2,038	33.9%
TikTok	248	4.1%
DISARM	101	1.7%

Task Types

Task Type	Examples	Percentage
Content Analysis	5,770	95.9%
Narrative Analysis	148	2.5%
DISARM Classification	101	1.7%

Data Split

Split	Examples	Percentage
Training	4,815	80.0%
Validation	601	10.0%
Test	603	10.0%

Example Entry

Input

{
  "instruction": "Classify the following content according to DISARM Framework techniques and meta-narratives:",
  "input_text": "A viral WhatsApp broadcast claims that the BVAS (Bimodal Voter Accreditation System) machines have been pre-loaded with votes by INEC in favour of the incumbent party. The message includes a grainy, out-of-context video of an INEC official handling a machine.",
  "output": "{\n  \"meta_narrative\": \"Compromised Election Technology\",\n  \"primary_disarm_technique\": \"T0022.002: Develop Original Conspiracy Theory Narratives\",\n  \"confidence_score\": 0.95,\n  \"key_indicators\": [\n    \"BVAS\",\n    \"INEC\",\n    \"pre-loaded\",\n    \"rigged\",\n    \"incumbent\",\n    \"WhatsApp broadcast\",\n    \"out-of-context video\"\n  ],\n  \"platform\": \"WhatsApp\",\n  \"language\": \"en\",\n  \"category\": \"Undermining Electoral Institutions\"\n}"
}

Output Structure

The model outputs structured JSON with the following fields:

• meta_narrative: The overarching narrative theme
• primary_disarm_technique: Main DISARM Framework technique
• confidence_score: Classification confidence (0-1)
• key_indicators: Relevant keywords and phrases
• platform: Source platform
• language: Content language
• category: Content category

Usage

With Hugging Face Datasets

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("ArapCheruiyot/disarm-election-watch-dataset")

# Access different splits
train_data = dataset["train"]
validation_data = dataset["validation"]
test_data = dataset["test"]

# Example usage
for example in train_data:
    print(f"Instruction: {example['instruction']}")
    print(f"Input: {example['input_text']}")
    print(f"Output: {example['output']}")
    break

Direct Download

# Download the complete dataset
huggingface-cli download ArapCheruiyot/disarm-election-watch-dataset unified_training_dataset.json

# Download MLX-LM format files
huggingface-cli download ArapCheruiyot/disarm-election-watch-dataset train.jsonl
huggingface-cli download ArapCheruiyot/disarm-election-watch-dataset valid.jsonl
huggingface-cli download ArapCheruiyot/disarm-election-watch-dataset test.jsonl

For Fine-tuning

# Load for MLX-LM fine-tuning
from mlx_lm import load, generate

# The dataset is already in the correct format for MLX-LM
# Use the train.jsonl, valid.jsonl, and test.jsonl files

DISARM Framework

The dataset uses the DISARM Framework for classification, which categorizes disinformation techniques into:

Meta-Narratives

• Compromised Election Technology
• Ethnic Mobilization
• Character Assassination
• Foreign Interference
• Economic Collapse
• And more...

DISARM Techniques

• T0022: Develop Original Conspiracy Theory Narratives
• T0135: Polarise
• T0086: Deceptively Edit Images
• T0003: Spread Rumours of Manipulation
• And more...

Data Collection

Sources

• Social Media Platforms: Telegram, X/Twitter, TikTok
• Election Monitoring: Official election monitoring reports
• Fact-Checking Organizations: Verified fact-checking databases
• Academic Research: Peer-reviewed research on election disinformation

Annotation Process

1. Content Collection: Systematic collection from multiple platforms
2. Expert Annotation: DISARM Framework experts classify content
3. Quality Control: Multi-stage review process
4. Validation: Cross-validation by multiple annotators

Limitations

Known Limitations

1. Language: Primarily English content
2. Geographic Focus: Nigerian election context
3. Temporal Context: Specific election periods
4. Platform Coverage: Limited to specific social media platforms

Potential Biases

1. Cultural Context: May not generalize to other cultures
2. Platform-Specific: Platform-specific nuances may be missed
3. Evolving Tactics: May not capture new disinformation techniques

Citation

If you use this dataset, please cite:

@misc{disarm_election_watch_dataset_2025,
  title={DISARM Election Watch Dataset: Election Disinformation Classification},
  author={Robin Kiplangat},
  year={2025},
  url={https://huggingface.co/datasets/ArapCheruiyot/disarm-election-watch-dataset}
}

License

This dataset is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• DISARM Framework: For the classification methodology
• Election Monitoring Organizations: For data collection support
• Fact-Checking Organizations: For verification support
• Academic Researchers: For methodological guidance

Contact

For questions, issues, or collaboration opportunities:

• Dataset Repository: ArapCheruiyot/disarm-election-watch-dataset
• Model Repository: ArapCheruiyot/disarm-ew-llama3-finetuned

Version History

• v1.0.0: Initial release with 6,019 examples
• Sources: Telegram, X/Twitter, TikTok, DISARM
• Format: Alpaca JSON + MLX-LM JSONL
• Annotation: DISARM Framework classification

Ollama Usage

Quick Start with Ollama

The model is also available for local deployment using Ollama:

# Create Ollama model
ollama create disarm-ew-llama3-finetuned -f Modelfile

# Run the model
ollama run disarm-ew-llama3-finetuned "Your prompt here"

Example Usage

ollama run disarm-ew-llama3-finetuned "### Instruction:
Classify the following content according to DISARM Framework techniques and meta-narratives:

### Input:
A viral WhatsApp broadcast claims that the BVAS machines have been pre-loaded with votes by INEC in favour of the incumbent party.

### Response:"

Expected Output

{
  "meta_narrative": "Compromised Election Technology",
  "primary_disarm_technique": "T0022.001: Develop False Conspiracy Theory Narratives about Electoral Manipulation and Compromise",
  "confidence_score": 0.98,
  "key_indicators": ["BVAS", "pre-loaded", "INEC"],
  "platform": "WhatsApp",
  "language": "en",
  "category": "Undermining Electoral Institutions"
}

Local Deployment Benefits

• Privacy: Run locally without sending data to external servers
• Speed: Fast inference on local hardware
• Customization: Modify prompts and parameters as needed
• Offline: Works without internet connection