Open AccessLeveraging Publicly Available Information to Analyze Information Operations
Author(s) -
Nico Manzonelli,
T Brown,
Antonio Avellandea-Ruiz,
William A Bagley,
Ian Kloo
Publication year - 2022
Publication title -
industrial and systems engineering review
Language(s) - English
Resource type - Journals
ISSN - 2329-0188
DOI - 10.37266/iser.2021v9i2.pp142-148
Subject(s) - computer science , subject matter , the internet , subject (documents) , data science , focus (optics) , subject matter expert , world wide web , information retrieval , artificial intelligence , expert system , psychology , pedagogy , physics , optics , curriculum
Traditionally, a significant part of assessing information operations (IO) relies on subject matter experts’ time- intensive study of publicly available information (PAI). Now, with massive amounts PAI made available via the Internet, analysts are faced with the challenge of effectively leveraging massive quantities of PAI to draw meaningful conclusions. This paper presents an automated method for collecting and analyzing large amounts of PAI from China that could better inform assessments of IO campaigns. We implement a multi-model system that involves data acquisition via web scraping and analysis using natural language processing (NLP) techniques with a focus on topic modeling and sentiment analysis. After conducting a case study on China’s current relationship with Taiwan and comparing the results to validated research by a subject matter expert, it is clear that our methodology is valuable for drawing general conclusions and pinpointing important dialogue over a massive amount of PAI.