Abstract • Performed an investigation of current literature to identify currently existing technologies. • Interviewed members of moderation teams to gather information and identify the projects requirements based on feedback. • Developed a bot to perform sentiment analysis on messages sent on an online messaging platform • Performed testing on the Model used in the Bot to identify its overall accuracy using an ROC Curve • Evaluated the effectiveness of the Bot by comparing it to a current generation bot that uses a word list and a Human Moderator.
Project Details • Evaluated the effectiveness of Sentiment Analysis enabled moderation bots in online communities. • Project Aims • To investigate what people, specifically part of a moderation team, want from a moderation bot. • Evaluate whether an AI package like TensorFlowJS/Toxicity is enough to support human moderators on Discord. • To design, plan, develop and then deploy a discord moderation bot with the capability to detect toxic language through sentiment to assist community moderation teams with the moderation of their communities more autonomously. • To evaluate the performance of the produced moderation bot by testing it to gauge its effectiveness and how it meets the initial criteria for success.
• Analysis was conducted around current technologies and an investigation was performed into what moderators would like featured in a bot outlined by this project.
Project Details • Design consisted of identifying a suitable development stack, followed by creating documentation outlining its features and data flow using UML Diagrams. • Implementation allowed for the features to be implemented, following OOP and Modern ES6 standards to produce high-quality code. • Experiments and Testing involved an informed consent “room” where willing participants could interact with the bot. Testing also included passing a test data set and comparing its results to gain an understanding of its accuracy.
Project Results • The project revealed that the Bot developed was capable of assisting moderators with their role and that it was effective enough at detecting toxicity that it would be worth implementing into their ecosystem. • The bot using the AI Model was revealed to be more accurate than a generic wordlist bot although at a slight speed disadvantage that could be overcome by implementing caching in the future.
Conclusion • The original question was successfully answered. • The bot proved effective in a live testing environment interacting with consenting members. • The Bot was more accurate than the Word List Bot. • In Future, other models could be implemented as better models are created. • Combining both an AI Model and a Word List would provide the best solution.