Automated detection of offensive language behavior on social networking sites

Social Networking Sites are booming as never before. Apart from the numerous new opportunities that are provided, also hazards such as messages containing sexual harrasment or racist attacks have to be taken into account. Since manually monitoring and analysing all messages seperately is unattainable, solutions using automated methods are sought. This study applies machine learning techniques to perform automated offensive language detection. Offensive language can be defined as “expressing extreme subjectivity“ and this study mainly focuses on two categories ’sexual’ and ’racist’. A corpus, originating from the Dutch distribution of the social network Netlog, is used and contains over seven million blog messages. We note that only a very small amount (approximately 0.85%) of these blog messages can be defined as messages that contain abusive language. Initially, the intention is to implement two supervised learning methods Naive Bayes and Support Vector Machine. These methods base the classification of a message on previous experiences, derived from a labeled training set. To build such training set offensive messages should be efficiently extracted out of the corpus. In order to achieve this, an information retrieval system, expanded with a query expansion technique, is applied. A query containing offensive terms delivers offensive messages, however a more efficient approach is considered by enhancing the query using Rocchio query expansion. This study shows that using query expansion can effectively increase the amount of relevant messages retrieved. These supervised classifiers are trained on the labeled set and afterwards their performance is tested on an independant validation set. The Naive Bayes classifier does not perform well on the validation set and is therefore disregarded in the further analysis. Our Support Vector Machine implementation achieves results of approximately 69% precision and 62% recall. However, these results are obtained by ignoring very small messages, since SVM has difficulties classifying messages that do not contain much information. To tackle the issues SVM suffers from a more reliable, but less dynamic method is designed, based on word lists. This method, that is named a semantic classifier, obtains