Machine Learning for the Automated Identification of Cyberbullying and Cyberharassment
Cyberbullying and cyberharassement are a growing issue that is straining the resources of human moderation teams. This is leading to an increase in suicide among the affected teens who are unable to get away from the harassment. By utilizing n-grams and support vector machines, this research was able to classify YouTube comments with an overall accuracy of 81.8%. This increased to 83.9% when utilizing retraining that added the misclassified comments to the training set. To accomplish this, a 350 comment balanced training set, with 7% of the highest entropy 3 length n-grams, and a polynomial kernel with the C error factor of 1, a degree of 2, and a Coef0 of 1 were used in the LibSVM implementation of the support vector machine algorithm. The 350 comments were also trimmed with a k-nearest neighbor algorithm where k was set to 4% of the training set size. With the algorithm designed to be heavily multi-threaded and capable of being run across multiple servers, the system was able to achieve that accuracy while classifying 3 comments per second, running on consumer grade hardware over Wi-Fi.^
Daniel N Ducharme,
"Machine Learning for the Automated Identification of Cyberbullying and Cyberharassment"
Dissertations and Master's Theses (Campus Access).