Developing Pattern and Anomaly Detection Methods in Influence Campaigns

Influence operations are a prominent psychological component of modern warfare. Recent historic events including the 2016 US election, the 2021 Myanmar Coup, and the Russian invasion of Ukraine in early 2022 have catapulted Department of Defense (DoD) interest in modeling and predicting outcomes of military and political events, particularly in regions of strategic interest to the US. The MIT Lincoln Laboratory, Group 52, under contract with USTRANSCOM, has developed the Global Influence Model (GIM) to evaluate the information landscape at scale. This project seeks to expand on the previous work of Group 52 on GIM, incorporating pattern and anomaly detection methods. Several statistical and machine learning methods were applied to a data set of approximately 30,000 news articles from a 2-year period between August 2019 and August 2021. Statistical methods included moving average models and Singular Spectrum Analysis (SSA). Machine learning techniques included the use of an autoencoder and an LSTM neural network. These methods provide different ways to visualize and characterize the data. Together, the approaches offer a holistic picture of events in specific countries over a time period of interest. The figures generated by these techniques may be a useful tool for a military intelligence analyst. These products allow for the rapid visualization of large news article data sets that can help model influence campaigns as they unfold.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Mitchell, William B.

Advisors dc:contributor.advisor

• Harnasch, Raul
• Ross, Dennis

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/152868

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/152868