Detection of Political Manipulation through Unsupervised Learning
Sihyung Lee · KSII Transactions on Internet and Information Systems · 2019
Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections.Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives.Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data.However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics.We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy.The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier.It also models each opinion with features deduced from raw data with no additional processing.To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea.The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions.The system also effectively identified transitions in manipulative tactics over time.We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.