Who is Mohamed Soufan?

About

Mohamed Soufan (محمد صوفان) is a computational researcher and software engineer whose work uses data to understand society, human behavior, and complex systems across a wide range of real-world contexts.

His work uses computational methods and large-scale data to uncover patterns in human behavior that are often difficult to see directly. He is interested in finding unexpected relationships, unmet needs, and emerging behaviors within complex datasets, and in turning those findings into practical insights. His research can span business, society, media, government, organizations, and digital platforms, depending on where the data leads.

Alongside his research, Soufan designs and builds software systems, data pipelines, and data-collection infrastructure for analyzing large-scale datasets. His work integrates programming, statistical analysis, and natural language processing (NLP) to transform raw data into structured insights.

Research Focus

His research uses computational methods and large-scale data to uncover patterns in human behavior, decision-making, and complex systems. Rather than being limited to a single domain, his work applies data-driven analysis wherever meaningful patterns can be extracted from real-world datasets.

Research Areas:

• Computational and Data-Driven Research
• Human Behavior and Decision-Making
• Computational Social Science
• Natural Language Processing (NLP)
• Media, Digital Platforms, and Online Behavior
• Applied Data Analysis for Business, Government, and Organizations

His research focuses on identifying unexpected relationships, emerging behaviors, and unmet needs within complex datasets, and translating those findings into practical insights.

Publications

He publishes open-access research examining digital discourse and social media dynamics.

His recent work analyzes linguistic uncertainty in Arabic-language posts on X (formerly Twitter) and its relationship to user engagement. The study identifies what he terms the uncertainty-reply asymmetry — the tendency for posts expressing uncertainty to generate disproportionately higher reply engagement.

Publications are available on arXiv, with research indexed through Google Scholar. Summaries and explanations are published on the Papers page.

In addition to academic papers, he publishes data-driven commentary and interpretations of his findings in the Analysis section.

Research Profiles

Soufan’s publications, datasets, and citation records are available through the following research platforms:

• ORCID
• arXiv
• SSRN
• Zenodo
• NASA ADS
• GitHub
• Google Scholar
• Website
• ResearchGate

Editorial Publications

• Fair Observer
• Eurasia Review
• Global Voices
• International Policy Digest

Collaboration & Media

Soufan’s research is designed to be accessible beyond academia and relevant to journalists, analysts, researchers, businesses, and organizations interested in data-driven insights, human behavior, digital communication, and social media.

He is open to:

• Academic collaboration
• Media commentary and interviews
• Data-driven research partnerships
• Analysis of digital discourse and online engagement trends

For collaboration inquiries or media requests, please contact Mohamed Soufan via email or the Contact page.