Defending Digital Discourse: Developing a Toxic Comment Classifier for Fostering Healthy Online Communities

To an extent, trolls or abusive users tend to penetrate the online community and ruin the potential healthy interactions that members and users can have; they over-engage members in the virtual space. In this regard, our work aims to develop models for the automatic detection and classification of toxic comments. The study is divided into four stages or executed in four steps. The first step is data preparation, which is done in stages; the data is loaded and preprocessed. The second step comprises Exploratory Data Analysis (EDA), where we seek to describe the toxic labels in the data and how they vary. The text is then standardized using text preprocessing techniques such as lower casing and punctuation removal before model training. For the model training tasks, logistic regression and Naive Bayes models are used to label each category of the toxicity classifier. It was observed that more than 96% of accuracy is achieved across varied categories: 96.9% of toxic comments, 97.2% of severe toxicity, 97.7% of obscenity, 98.9% of threats, 97.1% of insults, and 96.9% of identity hate. The models were very robust; the whole work took only 2 minutes and 58.24 seconds, which is an indication of its effectiveness and scalability.