About |
Mohamed Soufan is a computational researcher and software engineer working on large-scale data analysis and digital communication research.
His work focuses on collecting and analyzing large datasets from online platforms to study patterns in public discourse, information dynamics, and user engagement. Using computational methods, he examines how discussions evolve in digital environments and how social media structures public conversation. In his research, he introduced the concept of “uncertainty-reply asymmetry,” describing how expressions of uncertainty in online posts can generate disproportionate conversational engagement.
Alongside research, he designs and builds software systems, data pipelines, and scraping infrastructure for acquiring and processing large-scale digital datasets. His work combines programming, statistical analysis, and natural language processing to turn raw online data into structured analytical insights.
He publishes open-access research on arXiv, with work indexed through Google Scholar and shared on academic platforms including Academia.edu. He is also open to software engineering, data analysis, and research collaborations involving large-scale digital data.