Estimating the Prevalence of Religious Content in Intelligent Design Social Media

Abstract

Can machine learning prove useful in deciding sociological questions that are difficult for humans to judge impartially? We propose that it can, and even simple methods can be useful for evaluating evidence with reduced influence from human bias. Our case study is intelligent design (ID) social media, particularly the detection of religious content therein. Being a polarizing topic, critics of intelligent design claim that all intelligent design output consists of religious content, whereas defenders argue that ID is primarily motivated by scientific, not religious, concerns. To help determine where the truth lies, we use classifiers trained on the topically categorized 20 newsgroups dataset, applying the trained learners to automatically classify ID blog documents. As a control, we perform the same analysis on documents drawn from prominent mainstream evolutionary science blogs. Our classification results demonstrate a significant portion of religious and political content in the intelligent design dataset as judged by a non-human classifier, and a similarity in the proportion of documents assigned to religious and political categories in the evolutionary science blog dataset, likely indicating a dependence of discussion topics within the two communities.