You are encouraged to attend the defence. The details of the defence and attendance information is included below: 

Date:  November 21, 2024
Time: 10:00 AM to 12:00 PM (PT)
Defence mode: Remote

LINK TO JOIN:  Please contact the Office of Graduate Administration for information regarding remote attendance for online defences.

To ensure the defence proceeds with no interruptions, please mute your audio and video on entry and do not inadvertently share your screen. The meeting will be locked to entry 5 minutes after it begins: ensure you are on time. 

Thesis/dissertation entitled:   A Novel Naive Bayes Classifier for Detecting AI-Generated Text Using Word Pair Probabilities

Abstract: Nowadays, in the case of precise AI language models that can generate text without human intervention, distinguishing human-written content from AI-generated ones becomes crucial in such areas as education, blogging, and validating information sources. Common ways of text classification are often based on simple strategies like bag-of-words model or word lists as features. Consequently, these procedures fail to identify complex patterns or semantic relationships contained in natural languages because they concentrate on single terms.

This thesis introduces a new text classification method that uses term pairs as features within the framework of the original Multinomial Naive Bayes model to enhance its effectiveness. Naive Bayes classifiers are simple yet effective for classification, however as they treat each word independently, making it difficult to capture the complex relationships in language. For example, terms like "New York" or "artificial intelligence" have meanings that extend beyond their individual words. By incorporating term pairs, this method captures these relationships more effectively. This proposed AI detector distinguishes between human-written and AI-generated texts while also identifying the specific AI source, determining whether the text was generated by ChatGPT, Gemini, or classified as coming from less popular AI models under "OtherAI". Unlike known tools such as GPTZero or QuillBot, which only detect AI-generated content without identifying the model, this method provides a more detailed classification by identifying the specific AI that generated the text.

Examining Committee:  
Chair: Dr. Catharine Schiller, University of Northern British Columbia  
Supervisor: Dr. Liang Chen, University of Northern British Columbia  
Committee Member: Dr. Fan Jiang, University of Northern British Columbia  
Committee Member: Dr. Jianbing Li, University of Northern British Columbia  
External Examiner: Dr. Chunyi Gai, University of Northern British Columbia