Developing a Dataset for Personal Attacks and Other Indicators of Biases

Online argumentation, particularly on popular public discussion boards and social media, is rich with fallacy-and bias-prone arguments. An artificially intelligent tool capable of identifying potential biases in online argumentation might be able to address this growing problem, but what would it take to develop such a tool? In this paper, we attempt to answer this question by carefully defining both argumentative biases and fallacies, and laying out some guidelines for automated bias detection. After laying out a roadmap and identifying current bottlenecks, we take some initial steps towards relieving these limitations through the creation of a dataset of personal and ad hominem attacks in comments. Our progress in this direction is summarized.