lightweight-nsm-v3

# Lightweight NSM: Narcissism Detection Dataset and Evaluation Results (Version 2.0) ## Description This dataset contains 2,000 annotated social media posts from Mastodon used to train and evaluate the Lightweight NSM (Narcissism Social Media) classifier. Posts were collected from two hashtags (#selfie and #selfportrait) and annotated using the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th Edition) clinical framework for Narcissistic Personality Disorder. **Version 2.0 Updates:**- **Full anonymization**: All personally identifiable information (usernames, URLs, @mentions, image URLs) has been removed to ensure GDPR compliance and protect participant privacy- **Enhanced methodology documentation**: Added detailed description of data cleaning process (40,021 → ~14,000 posts) and stratified random sampling method (1,000 posts per hashtag)- **CSV format**: Dataset provided in CSV format for easy access, browser preview in Zenodo, and compatibility with spreadsheet applications ## Dataset Summary - **Total posts**: 2,000 (1,000 per hashtag)- **Narcissistic posts**: 330 (16.5%)- **Non-narcissistic posts**: 1,670 (83.5%)- **Collection period**: November-December 2025- **Platform**: Mastodon (7 major instances)- **Annotation method**: GPT-4.1-mini with DSM-5 criteria (≥2 criteria threshold)- **Model performance**: F1-score 0.69 (narcissistic class), Accuracy 0.89 ## Key Finding Posts tagged with #selfie exhibit a **7.5-fold higher narcissism rate** (29.1%) compared to #selfportrait posts (3.9%), suggesting distinct psychological motivations between validation-seeking and artistic self-expression. ## Files Included ### Underlying Data1. **mastodon_2000_annotated_anonymized.csv** (663 KB) - Complete anonymized annotated dataset with DSM-5 labels in CSV format2. **annotated_dataset_2000_posts_anonymized.csv** (663 KB) - Alternative CSV file with the same dataset for compatibility3. **evaluation_results.csv** (187 bytes) - Model performance metrics (F1, precision, recall, accuracy) ### Extended Data4. **confusion_matrix.png** (25 KB, 300 DPI) - Confusion matrix visualization (test set N=400)5. **feature_importance_top20.png** (35 KB, 300 DPI) - Top 20 most important features for classification6. **annotation_prompt_gpt4.txt** (4.6 KB) - GPT-4.1-mini annotation prompt with DSM-5 criteria definitions7. **data_collection_methodology.pdf** (369 KB) - Comprehensive 12-page methodology documentation including data collection, cleaning, sampling, annotation, and model training procedures8. **README.md** (7.9 KB) - Dataset documentation and usage instructions ## Source Code The complete source code for data collection, preprocessing, annotation, and model training is available on GitLab: **Repository**: https://gitlab.com/aleksandra.ata/lightweight-nsm **License**: Apache 2.0 Includes:- `Mastodon_Scraper_MultiHashtag_Top50K.ipynb` - Data collection from Mastodon API- `preprocessing_pipeline_FIXED.ipynb` - Data cleaning and stratified sampling- `training_evaluation_pipeline.ipynb` - Model training and evaluation- `best_model.pkl` - Pre-trained Logistic Regression classifier ## Citation If you use this dataset, please cite: ```Ata, A., & Kozak, K. (2026). Lightweight NSM: Narcissism Detection Dataset and Evaluation Results (Version 2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18484444``` ## Related Publication This dataset supports the research article: > Ata, A., & Kozak, K. (2026). A Lightweight Narcissistic Social Media (NSM) Detection Architecture Using TF-IDF and Logistic Regression. *Open Research Europe* (in press). ## Ethics and Privacy - All data was collected from publicly available Mastodon posts using the platform's official API- No private or protected content was accessed- **Full anonymization applied**: All usernames, profile URLs, @mentions, image URLs, and location markers have been removed- Posts are identified only by anonymous numerical IDs (anon_XXXXXX)- Research conducted in accordance with GDPR Article 89 and EU Horizon Europe ethical guidelines- Informed consent waived as data was publicly posted and fully anonymized prior to publication ## Funding This work was supported by the European Union's Horizon Europe research and innovation programme under grant agreement No. 101095654 (Trustworthy Hybrid Cognitive Systems - THCS). ## Contact For questions or data removal requests: **Aleksandra Ata** Email: [e-mail] Affiliation: DSW University of Lower Silesia, Wrocław, Poland ## License This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). --- **Keywords**: narcissism, social media, Mastodon, DSM-5, natural language processing, personality detection, clinical linguistics, mental health, text classification, logistic regression