Hate Speech Classification Using SVM and Naive BAYES
This work addresses the need for interpretable and efficient hate speech detection for social media platforms and regulators, though it is incremental as it applies existing methods to this domain.
The paper tackled the problem of automatically detecting hate speech online by proposing solutions using Support Vector Machine (SVM) and Naïve Bayes algorithms, achieving classification accuracies of approximately 99% for SVM and 50% for Naïve Bayes on the test set.
The spread of hatred that was formerly limited to verbal communications has rapidly moved over the Internet. Social media and community forums that allow people to discuss and express their opinions are becoming platforms for the spreading of hate messages. Many countries have developed laws to avoid online hate speech. They hold the companies that run the social media responsible for their failure to eliminate hate speech. But as online content continues to grow, so does the spread of hate speech However, manual analysis of hate speech on online platforms is infeasible due to the huge amount of data as it is expensive and time consuming. Thus, it is important to automatically process the online user contents to detect and remove hate speech from online media. Many recent approaches suffer from interpretability problem which means that it can be difficult to understand why the systems make the decisions they do. Through this work, some solutions for the problem of automatic detection of hate messages were proposed using Support Vector Machine (SVM) and Naïve Bayes algorithms. This achieved near state-of-the-art performance while being simpler and producing more easily interpretable decisions than other methods. Empirical evaluation of this technique has resulted in a classification accuracy of approximately 99% and 50% for SVM and NB respectively over the test set. Keywords: classification; hate speech; feature extraction, algorithm, supervised learning