Prof. Mohamed Zakaria Kurdi
University of Lynchburg in Virginia, USA
Biography: Dr. Kurdi is an Associate Professor of Computer Science at the University of Lynchburg in Virginia, USA. In addition to his Ph.D. in CS, he has an interdisciplinary background in Software Engineering, Cognitive Science, and Linguistics. Before joining the University of Lynchburg, he worked in several institutions in North America and Europe. His research interests are in text and data mining and their applications to areas like intelligent computer-assisted language education, authorship attribution, bioinformatics, and Social Network Analysis (SNA). He authored a two-volume textbook about Natural Language Processing (NLP) that was published in French and English. His recent work on text mining won two best paper awards and a nomination from three different international conferences.
Speech Title: Analysis of Topical Dynamics within a Social Network: application to the Enron dataset
Abstract: Social networks are becoming an essential part of people’s lives in modern societies. Most of the previous research on social networks focused on understanding their formal properties. Unlike previous works, this paper tries to uncover the semantic aspects of social networks by tagging them with precise, human interpretable topics. It is shown that such tagging helps shed light on several aspects of social networks. For example, at the ego-centric level, it is shown that, through the proposed concepts of topical specialization and balance, topic annotation can help build activity profiles of the users about the addressed topics within the network. Topics have also been shown to help understand the nature of the interactions between pairs of users. Finally, it is shown that topic tagging of social networks can help better understand the seasonality of the topics and the user’s change of focus over time. Besides helping better understand the social network, it is shown that they help improve some applications involving identifying central users and recommending a friend.