Document Type
Conference Proceeding
Publication Date
2016
Publication Title
The 15th IEEE International Conference on Machine Learning and Applications (ICMLA’16)
Abstract
Cyberbullying can have a deep and long lasting impact on its victims, who are often adolescents. Accurately detecting cyberbullying helps prevent it. However, the noise and errors in social media posts and messages make detecting cyberbullying very challenging. In this paper, we propose a novel pronunciation based convolutional neural network (PCNN) to address this challenge. Upon observing that the pronunciation of misspelled words in informal online conversations is often unchanged, we used the phoneme codes of the text as the features for a convolutional neural network. This procedure corrects spelling errors that did not alter the pronunciation, thereby alleviating the problem of noise and bullying data sparsity. To overcome class imbalance, a common problem in cyberbullying datasets, we implement three techniques that include threshold-moving, cost function adjusting, and a hybrid solution in our model. We evaluate the performance of our models using two cyberbullying datasets collected from Twitter and Formspring.me. The results of our experiment show that PCNN can achieve improved recall and precision compared to baseline convolutional neural networks.
DOI
DOI 10.1109/ICMLA.2016.71
Recommended Citation
Zhang, Xiang; Tong, Jonathan; Vishwamitra, Nishant; Whittaker, Elizabeth; Mazer, Joseph P.; Kowalski, Robin; Hu, Hongxin; Luo, Feng; Macbeth, Jamie C.; and Dillon, Edward, "Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network" (2016). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/387
Comments
Archived as published.
he 15th IEEE International Conference on Machine Learning and Applications (ICMLA’16), Anaheim, CA, December 18-20, 2016.