Nashwan Maher

Year: 2025

Academic Degree: Diploma

Supervisor Type: Supervisor

Supervisor State: Graduated

Khaleel Saab

Year: 2023

Academic Degree: Diploma

Supervisor Type: Supervisor

Supervisor State: Graduated

phd

Jul 1, 2015 - May 12, 2020

Doctor Of Philosophy in Computer Enginering and Sciences

Master

Jul 12, 2006 - Jun 12, 2008

Master's degree in computer information system

Bsc degree

Sep 1, 2002 - Jun 1, 2006

Bachler degree in computer engineering

Information Techniques Managment Department [department head]

Sep 1, 2023 - Present

manager, Director [Director of Scholarships And Cultural Relations]

Oct 14, 2021 - Sep 1, 2023

Director of Scholarships And Cultural Relations at Northern Technical University

Social media vocabulary reveals education attainment of populations

Mar 6, 2019

Journal (CompleNet 2019)

publisher Springer

DOI https://doi.org/10.1007/978-3-030-14459-3_13

Educational attainment is a major indicator of a nation’s development stage. Yet, often we have to rely on census information that is done at insufficient periodicity not capturing well the dynamics of certain regions. In this context, social media can act as a sensor of populations with up-to-date information. In this paper, we focus on revealing the relationship between social media vocabulary and educational attainment. This work was performed at the county level for 5 different geographical areas in the United States by comparing the education attainment according to the American community survey to the level of vocabulary used in social media in the same region for the same period of the census (2010–2015). Our results show that social media vocabulary level can reveal the educational attainment of a population for specific areas with a few exceptions concentrated in counties that have a high population density. As a secondary contribution, the sampling method to calculate the vocabulary level in social media may also help using large twitter datasets in the context of social sensing.

Read Publication

Characterization of written languages using structural features from common corpora

Feb 23, 2017

Journal (CompleNet 2017)

publisher Springer

DOI https://doi.org/10.1007/978-3-319-54241-6

For more than 5,000 years, we have been communicating using some form of written language. For many scholars, the advent of written language contributed to the development of societies because it enabled knowledge to be passed to future generations without considerable loss of information or ambiguity. Today, it is estimated that we use about 7,000 languages to communicate, but the majority of these do not have a written form; in fact, there are no reliable estimates of how many written languages exist today. There are three main families of written languages: Afro-Asiatic, Indo-European, and Turkic. These families of languages are based on historical family-trees. However, with the amount of data available today, one can start looking at language classification using regularities extracted from corpora of text. This paper focus on regularities of 10 languages from the mentioned families. In order to find features for these languages we use (1) Heaps’ law, which models the number of distinct words in a corpus as a function of the total number of words in the same corpora, and (2) structural properties of networks created from word co-occurrence in large corpora for different languages. Using clustering approaches we show that despite differences from years of being used in separate countries, the clustering still seem to respect some historical organization of families.

Read Publication