Publications

For the most up-to-date list, see Google Scholar.

Selected Publications

A Comparative Analysis of Transformer and Traditional ML Models for Cyberbullying Detection on Twitter (now X)
M. Abusaqer and J. Saquer. IEEE COMPSAC, 2025. doi: 10.1109/COMPSAC65507.2025.00216
Contribution: controlled comparison of TF-IDF baselines vs. transformers for cyberbullying detection, with attention to evaluation rigor and deployment trade-offs.
IEEE Xplore

BibTeX

@inproceedings{abusaqer_compsac2025_cyberbullying,
  author    = {Abusaqer, Muhammad and Saquer, J.},
  title     = {A Comparative Analysis of Transformer and Traditional ML Models for Cyberbullying Detection on Twitter (now X)},
  booktitle = {Proceedings of the IEEE Annual Computers, Software, and Applications Conference (COMPSAC)},
  year      = {2025},
  doi       = {10.1109/COMPSAC65507.2025.00216}
}

Twitter User Sentiments Analysis: Health System Cyberattacks Case Study
M. Abusaqer, M. B. Senouci, and K. Magel. IEEE ICAIIC, 2023. doi: 10.1109/ICAIIC57133.2023.10067026
Contribution: event-centric analysis of health-system cyberattack discourse with ML baselines and time-aware framing.
PDF

BibTeX

@inproceedings{abusaqer_icaiic2023_uhs_sentiment,
  author    = {Abusaqer, Muhammad and Senouci, M. B. and Magel, K.},
  title     = {Twitter User Sentiments Analysis: Health System Cyberattacks Case Study},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC)},
  year      = {2023},
  doi       = {10.1109/ICAIIC57133.2023.10067026}
}

| Details

A Curated Twitter/X Dataset of the 2020 UHS Ransomware Incident: Collection Protocols, Ethics, and Incident-Response Utility
M. Abusaqer and S. Ludwig. submitted to IEEE Access, Dec. 2025 (under review).
Contribution: reproducible collection + ethics + dataset documentation for incident-response utility.
A controlled evaluation of prompted LLM inference vs. fine-tuned encoders for text classification under reliability and cost constraints
M. Abusaqer. submitted to ICML 2026, Jan. 2026 (under review).
Contribution: reliability- and cost-aware evaluation framework for prompted LLM inference vs. fine-tuned encoders.
Green NLP for Online Abuse Detection: Accuracy–Latency–Energy Trade-offs of TF-IDF vs. Fine-Tuned Transformers
M. Abusaqer. submitted to ICCSIC 2026, Jan. 2026 (under review).
Contribution: energy-aware benchmarking to support environmentally and operationally responsible model selection.

Refereed Conference Papers

[1] M. Abusaqer, and J. Saquer, “A Comparative Analysis of Transformer and Traditional ML Models for Cyberbullying Detection on Twitter (now X),” Proc. IEEE COMPSAC, 2025. doi: 10.1109/COMPSAC65507.2025.00216. ( IEEE Xplore)
[2] M. Abusaqer, M. B. Senouci, and K. Magel, “Twitter User Sentiments Analysis: Health System Cyberattacks Case Study,” Proc. IEEE ICAIIC, 2023. doi: 10.1109/ICAIIC57133.2023.10067026. ( PDF | Details)
[3] A. Pun†, B. Olson†, and M. Abusaqer, “Predicting Student Academic Performance: Using Machine Learning and Clustering,” Proc. MICS, 2025. ( PDF)
[4] D. Degele†, and M. Abusaqer, “Analyzing Ransomware Incidents in Healthcare: Patterns and Risk Assessment,” Proc. MICS, 2025. ( PDF)
[5] T. Khan†, and M. Abusaqer, “Evaluating Quick-Commerce Platforms: A Sentiment and Topic Modeling Analysis of User Reviews,” Proc. MICS, 2025. ( PDF)
[6] J. Jensen†, and M. Abusaqer, “Global Echoes of the FIFA World Cup 2022: Sentiment and Theme Analysis via Deep Learning and Machine Learning on Twitter,” Proc. MICS, 2024. ( PDF | Details)
[7] S. Khan†, K. Khan†, and M. Abusaqer, “Text Detection between an AI-Written Passage vs. a Human Written Passage,” Proc. MICS, 2024. ( PDF | Details)
[8] T. Smith†, and M. Abusaqer, “Predicting Campus Crime Based on State Firearm Policy,” Proc. MICS, 2024. ( PDF | Details)
[9] M. Abusaqer, and C. Fofie†, “Cyberbullying Classification Using Three Deep Learning Models: GPT, BERT, and RoBERTa,” Proc. MICS, 2023. ( PDF | Details)
[10] M. Abusaqer, and Q. Sullivan†, “Darknet Traffic Classification Using Deep Learning,” Proc. MICS, 2023. ( PDF | Details)
[11] A. Scott†, J. T. Snow†, and M. Abusaqer, “Automated Categorization of Cybersecurity News Articles through State-of-the-Art Text Transfer Deep Learning Models,” Proc. MICS, 2023. ( PDF | Details)
[12] M. Abusaqer, and K. Magel, “Comparison of Students’ Learning and Engagement on an Online Course Before and After the Spread of COVID-19,” Proc. MICS, 2022. ( PDF | Details)
[13] M. Abusaqer, and K. Magel, “Teaching Computer Packages to Students Different in Everything,” Proc. MICS, 2019. ( PDF | Details)

Technical Report

[14] Y. Bhardwaj, M. Abu-Saqer, and M. A. Pérez-Quiñones, “General Interface Description of Websites Using CLICK and UIML,” Virginia Tech CS Tech. Rep. TR-04-27, 2004. ( PDF | Details)

Manuscripts (Under Review / In Preparation)

[15] M. Abusaqer, “A controlled evaluation of prompted LLM inference vs. fine-tuned encoders for text classification under reliability and cost constraints,” submitted to ICML 2026, Jan. 2026 (under review).
[16] M. Abusaqer, “Green NLP for Online Abuse Detection: Accuracy–Latency–Energy Trade-offs of TF-IDF vs. Fine-Tuned Transformers,” submitted to ICCSIC 2026, Jan. 2026 (under review).
[17] M. Abusaqer, “Caption-then-Classify for Multimodal Harmful Meme Detection: A Lightweight Baseline for Social-Good,” submitted to MM4SG 2026, Jan. 2026 (under review).
[18] M. Abusaqer and S. Ludwig, “A Curated Twitter/X Dataset of the 2020 UHS Ransomware Incident: Collection Protocols, Ethics, and Incident-Response Utility,” submitted to IEEE Access, Dec. 2025 (under review).
[19] M. Abusaqer, “TREST: Trust-Calibrated Relevance Scoring for Healthcare Cyber-Incident Tweets,” manuscript in preparation.
[20] M. Abusaqer and S. Ludwig, “CyberTweetGrader&Labeler: A Domain-Specific NLP System for Prioritizing Cyber-Incident Discourse on Twitter/X,” manuscript in preparation.

† student mentee