AN AUTOMATIC HATE SPEECH DETECTION IN SOCIAL MEDIA THROUGH COMPUTATIONAL LINGUISTICS: INFIDELITY VIDEOS IN FOCUS

Abstract

The escalating prevalence of hate speeches, amplified by the misguided use of social media, introduces alarming challenges to the safeguarding of human rights and individual welfare. Motivated by this, the study explored the detection and classification of hate speech, specifically as observed in speeches and comments related to infidelity videos on YouTube Channel of Raffy Tulfo in Action. Further, the study utilized a computational linguistic algorithm through Long Short-Term Memory (LSTM). Additionally, the study sought to understand the distinctions in linguistic features between hate speech and non-hateful speech through LSTM. The researcher used 9,600,586 tokens for the analysis. To answer the first research question, the employment of LSTM helped identify hate speeches from non-hate speeches through effective data gathering through YouTube Application Programming Interface (API) and Whisper AI, text processing, labeling, coding, and algorithm deployment. Through that process, LSTM also classified them per target, including sex, quality, physical attributes, disability, religion, race, and class. Further, to answer the second research question, the study was able to identify 35 lexicons. Some samples include peenoise, U10, kokey, taitok, quibolok, squami, and shut@, which were used negatively. Lastly, to answer the last question, tokenization, embedding, sequential dependencies, padding, training-testing, and evaluating helped LSTM assess hate speech linguistic features. It is evident in the confusion matrix showing 46% true positives and 49% true negatives and its evaluation performance of 95% F1 score, affirming its high robustness and reliability.