Abstract:
Detecting and controlling propagation of hate-speech over social media platforms is
a challenge. This problem is exacerbated by extreme 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 Tensor Flow Decision Forests (TF-DF), three models were developed
that is, Gradient Boosted Trees with Universal Sentence Embeddings (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%,
recall of 0.9587, precision of 0.9831, and AUC of 0.9984. Therefore, this model can be
utilized for detecting hate speech on social media platforms.
