A DEEP DECISION FORESTS MODEL FOR HATE SPEECH DETECTION

(Received: 2-Nov.-2022, Revised: 4-Feb.-2023 , Accepted: 14-Feb.-2023)

Authors Kennedy Malanga Ndenga,

Keywords #Hate speech detection #TensorFlow decision forests #Gradient boosted trees #Universal sentence embedding #National cohesion and integration commission

Abstract Detecting and controlling propagation of hate speech over social-media platforms is a challenge. This problem is exacerbated by extremely fast flow, readily available audience and relative permanence of information on social media. The objective of this research is to propose a model that could be used to detect political hate speech that is propagated through social-media platforms in Kenya. Using Twitter textual data and Keras TensorFlow Decision Forests (TF-DF), three models were developed; i.e., Gradient Boosted Trees with Universal Sentence Embedding (USE), Gradient Boosted Trees and Random Forest, respectively. The Gradient Boosted Trees with USE model exhibited a superior performance with an accuracy of 98.86%, a recall of 0.9587, a precision of 0.9831 and an AUC of 0.9984. Therefore, this model can be utilized for detecting hate speech on social media platforms.

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