Identification of Abusive Behavior Towards Religious Beliefs and Practices on Social Media Platforms

The ubiquitous use of social media has enabled many people, including religious scholars and priests, to share their religious views. Unfortunately, exploiting people’s religious beliefs and practices, some extremist groups intentionally or unintentionally spread religious hatred among different communities and thus hamper social stability. This paper aims to propose an abusive behavior detection approach to identify hatred, violence, harassment, and extremist expressions against people of any religious belief on social media. For this, first religious posts from social media users’ activities are captured and then the abusive behaviors are identified through a number of sequential processing steps. In the experiment, Twitter has been chosen as an example of social media for collecting dataset of six major religions in English Twittersphere. In order to show the performance of the proposed approach, five classic classifiers on n-gram TF-IDF model have been used. Besides, Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) classifiers on trained embedding and pre-trained GloVe word embedding models have been used. The experimental result showed 85% accuracy in terms of precision. However, to the best of our knowledge, this is the first work that will be able to distinguish between hateful and non-hateful contents in other application domains on social media in addition to religious context.