Automated inference of point of view from user interactions in collective intelligence venues
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):82-90, 2014.
Empirical evaluation of trust and manipulation in large-scale collective intelligence processes is challenging. The datasets involved are too large for thorough manual study, and current automated options are limited. We introduce a statistical framework which classifies point of view based on user interactions. The framework works on Web-scale datasets and is applicable to a wide variety of collective intelligence processes. It enables principled study of such issues as manipulation, trustworthiness of information, and potential bias. We demonstrate the model’s effectiveness in determining point of view on both synthetic data and a dataset of Wikipedia user interactions. We build a combined model of topics and points-of-view on the entire history of English Wikipedia, and show how it can be used to find potentially biased articles and visualize user interactions at a high level.