Detecting inappropriate content on social media platforms (A case of Twitter)

Abstract

Technological advancements have led to the diversification of communication through development and use of social media platforms such as twitter, Facebook and Instagram as a viable means of communication in the country. This has led to changes that have improved the way in which people communicate. However, these changes have also brought about rapid use of inappropriate content in the ways of communication for example; use of aggressive and abusive language online among users. Minimizing such inappropriate behavior on social platforms is vital and this has been done through detection of such content on the platform and issue adverse measures of such inappropriateness. Since this platform receives heavy amounts of data inform of posts, automotive methods are therefore used to identify the anticipated content on the platform for example detection, blocking, reporting. This research study focused on the inappropriate use of text regarding vulgar language on social media platforms which was done using Data Cleaning Algorithms that analyze, detect, report the inappropriate data sets that have been used. In conclusion the study took on a qualitative-based approach to collect data from the existing data sets because the platform(twitter) has depicted a high level of being inappropriately used in the recent which has created a need to detect such content.