View Proposal

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Proposer

Marwan Fuad

Title

Human or bot?

Goal

To develop an application or demo that can accurately distinguish between human-generated and bot-generated texts in online conversations and social media comments

Description

With the increasing prevalence of AI-driven bots in online spaces, discerning between human and bot-generated texts has become difficult and crucial. This project will explore natural language processing (NLP) techniques and machine learning to develop a system capable of detecting bot-generated content. The proposed solution will involve collecting a diverse dataset of human and bot conversations, training a model to identify key linguistic features and patterns, and developing an interface to demonstrate the detection capability. The final deliverable could be an application, a web-based tool, or a code repository, which provides real-time analysis and classification of text inputs. A similar project resulted in a peer-reviewed publication, this is the expected outcome of this project

Resources

Publicly available datasets from platforms like Kaggle, including human and bot conversation logs. Python, TensorFlow/PyTorch, scikit-learn, NLTK/spaCy for NLP

Background

Url

Difficulty Level

Challenging

Ethical Approval

Full

Number Of Students

2

Supervisor

Marwan Fuad

Keywords

bot detection, nlp, machine learning, social media, text classification, cybersecurity, ai ethics

Degrees

Bachelor of Science in Computer Science
Master of Science in Artificial Intelligence
Master of Science in Data Science