Algorithmically Supported Moderation in Children’s Online Communities

Tan, Flora. Algorithmically Supported Moderation in Children’s Online Communities. Thesis. Massachusetts Institute of Technology, 2017.


The moderation of harassment and cyberbullying on online platforms has become a heavily publicized issue in the past few years. Popular websites such as Twitter, Facebook, and YouTube employ human moderators to moderate user-generated content. In this thesis, we propose an automated approach to the moderation of online conversational text authored by children on the Scratch website, a drag-and-drop programming interface and online community. We develop a corpus of children’s comments annotated for inappropriate material, the first of its kind. To produce the corpus of data, we introduce a comment moderation website that allows for the review and label of comments. The web-tool acts as a data-pipeline, designed to keep the machine learning models up to date with new forms of inappropriate content and to reduce the need for maintaining a blacklist of profane words. Finally, we apply natural language processing and machine learning techniques towards detecting inappropriate content from the Scratch website, achieving an F1-score of 73%. 

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