Monitoring Social Media Content for Religious Context
Social Media these days has become the prime medium for religious debates. While some people utilise the medium wisely to talk about the rational thoughts associated with different religions, others might use it to marginalize or dehumanize certain religions. Therefore, there is a dire need to analyse content having a religious context so that appropriate actions can be taken by the social media or blogging platforms against people attempting to propagate unhealthy religious messages. Therefore, in this paper, a multi-module machine learning based algorithm is proposed to analyse the religious context in online social media content. To do that, data has been gathered from popular social media sites Twitter. A methodology is then developed to identify tweets that talk about religion. Once, it is detected if the tweet is religion themed or not, sentiment analysis is performed and then the nature of the tweets (offensive or not) is detected to get a deeper understanding of the content that is being posted online. Based on the evaluation of various classification models, it is found that Naive Bayes performs the best at detecting a religious theme in tweets, achieving an accuracy of 84%. On the other hand, XGBoost performs the best at detection of offensive content in the domain of religion. Our results identify online religious offensive content and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.