Abstract:
Social media is changing the face of communication and culture in society around the world.
People share their information, feelings, and emotions by using social media platform like
Facebook. As social media users increasing day to day, cyber hate and offensive speech’s using
social media platform are also increasing rapidly. Social media especially Facebook have a very
huge impact on the success or destruction to an individual or groups life. Detecting hate and
offensive speech is used for the individuals, group, company as well as the government to make
decision and take action on the posts and comments that contains violence contents that cause
crime and conflict among the people to be removed.
In the proposed work we designed and implemented RNN based hate and offensive speech
detection model for Afaan Oromo, based on posts and comments which are available on public
Facebook pages in Afaan Oromo. Dataset collected from Facebook public pages was contains
domains of politics, religious, and ethnic that contains hate and offensive speech content. We
collected 7000 dataset from Facebook public pages and dataset labeled by experts into three
classes: hate, offensive and neither. We performed various preprocessing techniques before its
feed to the recurrent neural network model. In this study, the recurrent neural network based Long
Short Term Memory (LSTM) model developed for the detection of posts and comments contains
hate and offensive text on Social media. Additionally word embedding was created by applying
the word2vec algorithm with a CBOW model, on a corpus collected from Facebook. The
experiment was conducted with LSTM models using 80% of the data set for training and 20% for
testing the model and selecting the best combination of hyper-parameters Finally, LSTM-based
RNN achieved promising result with accuracy 92% and F-score of 93% to detect posts and
comments as hate speech, offensive speech, or neither of them through training. Therefore LSTM
model is the best mechanism to detect hate speech and offensive language for Afaan Oromo posts
and comment on Facebook.
