Evaluating Semantic Feature Representations to Efficiently Detect Hate Intent on Social Media

Detecting malicious intent behavior such as sharing hate speech has become an important challenge for social networking platforms. The method of automated hate speech detection for social media posts is often challenged by the complexity of capturing the context of the user expression with potential hate intent. We hypothesize that semantic features can help enrich the context representation of word senses in a social media post for machine learning algorithms. This paper presents a novel empirical study of diverse semantic features for hate speech classification task on social media posts. Specifically, we present an extensive empirical analysis, where we test the features of vector space model representation for corpus-based semantics, neural word embedding representation for distributional semantics, and declarative knowledge patterns from external knowledge base for domain semantics. Our experimental results show that ensembling the diverse feature representations improves the efficiency of hateful behavior classification in contrast to the case of a single type of feature representation. Results on two popular Twitter datasets for the hate speech detection task showed a consistent performance gain for the classification models that were based on the hybrid feature representation (absolute gain in F1 score up to 3.0%). The application of the proposed method of combining diverse feature representations can help in improving social media analytics systems for monitoring human behavior.