ZUBAIR ADAM FOLOHUNSHO

Computer Science

At the Computer Science department office

Appointment on Visitation important

Topic: Investigating Social Media Profiling Techniques For User Behavior Analysis And Threat Detection

Description:

Introduction:In the ever-evolving landscape of cybersecurity, the intersection of data science and social media profiling presents a critical frontier for research. This study delves into the realm of user behavior analysis through the lens of social media posts, aiming to bolster cybersecurity measures by unraveling potential threats embedded in online interactions.

Aims/Objectives:The primary objective is to employ data science methodologies to develop a robust social media profiling framework for comprehensive user behavior analysis. This involves extracting patterns from users' posts, identifying anomalies, and correlating these insights with potential cybersecurity threats. Specific aims include enhancing anomaly detection algorithms and improving the accuracy of threat prediction based on social media activity.

Methodology:The research will adopt a multifaceted methodology, integrating natural language processing (NLP) techniques to extract semantic meaning from social media posts. Machine learning algorithms, particularly anomaly detection models, will be employed to discern patterns indicative of malicious intent. The methodology also involves data preprocessing, feature engineering, and model training/validation on diverse datasets representative of various cyber threats.

Expected Results: Anticipated outcomes include the development of an advanced social media profiling model capable of identifying subtle behavioral indicators of cybersecurity threats. The research expects heightened accuracy in predicting and preemptively mitigating potential risks based on users' online activities, contributing to a more proactive cybersecurity posture.

Contribution to Knowledge/Society: This research contributes significantly to the fields of data science and cybersecurity by pioneering an innovative approach to threat detection. The developed framework not only advances the understanding of user behavior analysis but also holds the potential to revolutionize proactive cybersecurity strategies. The findings have broader implications for securing digital spaces and protecting individuals and organizations from evolving cyber threats, thereby contributing to the overall resilience and security of the digital society.

#	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