Cyberbullying detection from tweets using deep learning

What is it about?

With the rise of social media platforms, an increasing number of cases of cyberbullying has remerged. Everyday, large number of people, especially teenagers, become the victim of cyber abuse. A cyberbullied person can have a long-lasting impact on his mind. Due to it, the victim may develop social anxiety, engage in self-harm, go into depression or in the extreme cases, it may lead to suicide. This paper aims to evaluate various techniques to automatically detect cyberbullying from tweets by using machine learning and deep learning approaches. We applied machine learning algorithms approach and after analyzing the experimental results, we postulated that deep learning algorithms perform better for the task. We used word-embedding techniques for word representation for our model training. Pre-trained embedding GloVe was used to generate word embedding. Different versions of Glove were used and their performance was compared. Bi-directional LSTM was used for classification. The dataset contains 35,787 labeled tweets. The Glove840 word embedding technique along with bidirectional LSTM provided the best results on the dataset with an accuracy, precision and F1 measure of 92.60%, 96.60%, and 94.20% respectively.

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