ZUBAIR FOLOHUNSHO ADAM

Computer Science

At the Computer Science department office

Appointment on Visitation important

Topic: Cybersecurity: Social Media Profiling Techniques For User Behaviour Analysis And Threat Detection

Description: This research explores the intersection of cybersecurity and social media analytics, with a focus on developing advanced profiling techniques to understand user behaviour and identify potential threats. The objective is to leverage data mining, machine learning, and natural language processing NLP methodologies to analyse user-generated content, interaction patterns, and digital footprints across various social media platforms.Key areas of investigation include behavioural modelling, anomaly detection, sentiment analysis, and the identification of malicious actors or coordinated disinformation campaigns. The research aims to construct robust frameworks that can accurately profile user intentions whether benign or malicious by examining temporal activity patterns, language cues, network structures, and metadata.The outcomes of this research hold significant implications for improving digital forensics, enhancing automated threat detection systems, supporting law enforcement and intelligence operations, and contributing to the broader discourse on ethical data use and privacy-preserving surveillance in online spaces.

#	Certificate	School	Year
1.	Ph.D (Computer Science)	Department of Computer Science, Lagos State Univeristy	2023

Geospatial Inference through Social Media Text Analysis: A Data Science Approach for Identifying Subjects State of Origin

Introduction: In the digital age, the abundance of user-generated content on social media platforms presents a unique opportunity for research. This study focuses on leveraging data science to identify an individual's state of origin through an analysis of their social media chat history. The aim is to uncover subtle linguistic patterns indicative of specific geographical regions.

Aims/Objectives: This research seeks to develop a robust methodology for accurately determining a subject's state of origin based on their social media text data. Key objectives include identifying region-specific linguistic features, establishing a reliable classification algorithm, and testing the model's accuracy across diverse demographic and linguistic contexts.

Methodology: The study will employ a comprehensive methodology, involving the collection of a representative dataset from major social media platforms. Preprocessing steps, including text cleaning and feature extraction, will be followed by the application of advanced natural language processing (NLP) techniques and machine learning algorithms such as deep neural networks.

Expected Results: Anticipated outcomes include the successful development of a predictive model with high accuracy in identifying users' state of origin. The research aims to reveal region-specific linguistic markers and explore their significance in user-generated content. Insights into model limitations will inform future refinements.

Contribution to Knowledge/Society: This research contributes to scientific knowledge and societal understanding by applying data science to social media analysis. The methodology's potential to enhance geographic inference from online communications addresses a current research gap and has implications for sociolinguistics, cybersecurity, and public policy. The findings may prompt discussions on privacy and responsible use of personal data in the digital communication era.

ZUBAIR ADAM is a Lecturer II at the Department of Computer Science

ZUBAIR has a Ph.D in Computer Science from Department of Computer Science, Lagos State Univeristy