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Activity Number: 630 - Uncertainty Quantification, Reliability and Robust Inference
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Defense and National Security
Abstract #329062 Presentation
Title: Multivariate Methods and Data Integration in Social Media for Anomaly Detection
Author(s): Karl Pazdernik and Kellie MacPhee and Bryan Stanfill and Lisa Bramer*
Companies: Pacific Northwest National Laboratory and University of Washington and Pacific Northwest National Laboratory and Pacific Northwest National Laboratory
Keywords: anomaly detection; multivariate; principal component analysis; robust; streaming; tensor

Social media analytics have witnessed an explosion in the literature due to the growing popularity of social media platforms such as Twitter, Facebook, and Instagram. In particular, social media has been documented as a useful resource in problems relevant to national security, such as health monitoring, crime, and counter-terrorism. In this presentation, we focus on the specific statistical task of anomaly detection in multiple sources of streaming data. We compare a variety of anomaly detection techniques and discuss the impact of utilizing the hierarchical structure of temporal and multivariate data. Finally, we show how this methodology can be used for anomaly detection problems relevant to national security.

Authors who are presenting talks have a * after their name.

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