The
U.S. Special Operations Command posted a request for information for a Misinformation,
Disinformation and Mal-Information Campaign Detection Prototype. Responses are
due by 12:00 p.m. Eastern on January 13.
The Headquarters, United States
Special Operations Command (HQ USSOCOM) is conducting market research in
accordance with FAR 15.201. THIS IS NOT A REQUEST FOR PROPOSAL
(RFP).
United States Special Operations
Command (USSOCOM) has a requirement for a Commercial Off the Shelf (COTS)
software solution, to be used as a prototype for use of understanding the
information environment that can detect misinformation, disinformation and
mal-information campaigns in near to real-time to directly support information
operations within Special Operations Command. The resulting analysis will
be surfaced on a user interface and contextualized by the relevant narratives
and network of accounts. This software must leverage an intuitive cloud-hosted
user interface software which provides processing and analyzing multi-modal
social media and web data and has the programmatic ability to dissect and
categorize information sectors. The government requires a prototype data
pipeline that will identify viral and trending content for threat assessments
and score data with a ranking system that highlights the likelihood of being
fake or deceptive and display the information on the user interface.
The prototype will use a combination
of deep learning, natural language processing, and dynamic network analysis to
detect and examine the cross-platform spread of disinformation (in the form of
images, video, and text) over vast and disparate data sources and inform teams
of information which is intended to be deceptive. The vendor must address
anomaly detection, deep fake, foreign influence and anticipatory analytics,
leveraging deep learning, natural language processing, and proven machine
learning development and capabilities.
The resulting analysis will be
surfaced on a user interface and contextualized by the relevant narratives and
network of accounts. The system is designed to improve over time by
incorporating user feedback and adapt to the rapidly changing tactics of an
adversary.
The vendor must use network graph analysis to narrow focus to a relatively small target subset, which will result in a reduced computational load for the deep fake detectors. The analysis will be required to incorporate analyst feedback for a human-in-loop pattern to improve the models over time, leveraging supervised machine learning. In addition, because the analysis will include not only the original content but also the fuller network context, the developed prototype is expected to deliver a much richer and nuanced set of results to better understand and identify misinformation/disinformation across the information environment.
Full information is available here.
Source: SAM
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