Cyberbullying Detection using Ensemble Method

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Creator: 

Kadamgode Puthenveedu, Saranyanath

Date: 

2022

Abstract: 

Cyberbullying can be defined as a form of bullying that occurs across social media platforms using electronic messages.These platforms provide a ground for the cyberbullies to engage in bullying activities.State-of-the-art technologies such as Machine learning, NLP and Deep learning can be used to develop models that can detect cyberbullying.This dissertation proposes three different approaches and five models based on these technologies to identify cyberbullying using a newly generated email dataset.Our initial approach consists in using a traditional supervised machine learning.Our second approach is based on DistilBERT.Our last approach employs an ensemble technique. Our initial approach led to the implementation of two SVM models, one using TF-IDF feature extraction, the other using a combination of different tokens of TF-IDF vectors. Our third model was implemented using DistilBERT word embeddings. The highest accuracy was obtained using an ensemble model and the lowest accuracy was obtained using the SVM model with simple TF-IDF.

Subject: 

Computer Science

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Computer Science: 
M.C.S.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Computer Science

Parent Collection: 

Theses and Dissertations

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