Assistant Professor Jana Diesner spoke at the ninth International Conference on Web and Social Media (ICWSM) on May 27. Hosted annually by the Association for the Advancement of Artificial Intelligence, ICWSM addresses themes in social and computational sciences related to human social behavior on the web. The 2015 conference was held May 26-29 at the University of Oxford.
Diesner presented research conducted in collaboration with informatics doctoral student Craig Evans and GSLIS doctoral student Jinseok Kim in a talk titled, “Impact of Entity Disambiguation Errors on Social Network Properties.”
Abstract: Entities in social networks may be subject to consolidation when they are inconsistently indexed, and subject to splitting when multiple entities share the same name. How much do errors or shortfalls in entity disambiguation distort network properties? We show empirically how network analysis results and derived implications can tremendously change depending solely on entity resolution techniques. We present a series of controlled experiments where we vary disambiguation accuracy to study error propagation and the robustness of common network metrics, topologies, and key players. Our results suggest that for email data, not conducting deduplication, e.g. when operating on the level of email addressed instead of individuals, can make organizational communication networks appear to be less coherent and integrated as well as bigger than they truly are. For copublishing networks, improper merging as caused by the commonly used initial based disambiguation techniques can make a scientific sector seem more dense and cohesive than it really is, and individual authors appear to be more productive, collaborative and diversified than they actually are. Disambiguation errors can also lead to the false detection of power law distributions of node degree, suggesting preferential attachment processes that might not apply.