dc.contributor.author |
Ndenga, K. M. |
|
dc.date.accessioned |
2023-03-29T14:06:11Z |
|
dc.date.available |
2023-03-29T14:06:11Z |
|
dc.date.issued |
2023-03 |
|
dc.identifier.uri |
http://repository.kyu.ac.ke/123456789/960 |
|
dc.description.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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
6th Annual International Conference-2023, Kirinyaga University, Virtual |
en_US |
dc.subject |
- Hate Speech Detection, Tensor Flow Decision Forests, Gradient Boosted Trees, Universal Sentence Embeddings, National Cohesion and Integration Commission. |
en_US |
dc.title |
A deep decision forests model for hate speech detection. |
en_US |
dc.type |
Article |
en_US |