| NSF Org: |
SMA SBE Office of Multidisciplinary Activities |
| Recipient: |
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| Initial Amendment Date: | September 5, 2018 |
| Latest Amendment Date: | September 5, 2018 |
| Award Number: | 1831481 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sara Kiesler
skiesler@nsf.gov (703)292-8643 SMA SBE Office of Multidisciplinary Activities SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | September 15, 2018 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $209,486.00 |
| Total Awarded Amount to Date: | $209,486.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
450 JANE STANFORD WAY STANFORD CA US 94305-2004 (650)723-2300 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
CA US 94305-2004 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Secure &Trustworthy Cyberspace |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
This project will develop an integrated research framework for sociotechnical cybersecurity research and broader investigations of information provenance by behavioral, information, and computer scientists. Currently, researchers are mainly limited to natural language processing of large bodies of online text. This project will make it possible to analyze larger information worlds, including those from such countries as China, and the flow of information, including video and audio information, in newspapers, TV, and online sources. The project addresses a core goal of cybersecurity research, which is to understand the provenance, flow, and termination of information warfare, and censorship.
The project is aimed at constructing an integrated and unified information database that combines mass communication data from TV and print sources from six locations, with data from two popular online communication platforms. The project will generate a variety of metadata and time series data on topics, actors, events, and sentiments presented in communications by automated multimodal content analysis using text, image, video, and audio. Variables will be linked to identify trajectories of information flow between communication channels through multiple platforms. It will develop a new class of computational models and algorithms that can automatically analyze both verbal and nonverbal communications data by machine learning, computer vision, deep learning, and natural language processing. This project will allow researchers across the computational and social sciences to access the metadata and time series data through a search interface for qualitative research, a statistical package for quantitative research, and various visualization tools. This project will therefore link previously untapped data sources using cutting-edge computational methods to enable scholars to conduct systematic research on large-scale patterns in the emerging information and communication ecosystem.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The main goal of this project was to develop integrated and unified news databases and identify trajectories of inter-media flow through multiple media platforms. We achieved this objective, which in turn allowed us to systematically study, for the first time, the flow of information from the global media ecosystem into China. We develop a semi-automated system that combines deep learning and human annotation to find co-occurring content between global and Chinese social media platforms. We find that approximately one-fourth of content with relevance for China that gains widespread global public attention makes it way to Chinese social media. Chinese state-controlled media and commercialized domestic media play a dominant role in facilitating these inflows of information. However, Weibo users without traditional media or government affiliations are also an important mechanism for transmitting information into China. These results imply that while online censorship combined with control over traditional media provide substantial leeway for the Chinese government to set the agenda, social media provides opportunities for non-institutional actors to influence the information environment. Methodologically, the system we develop offers a new approach for the quantitative analysis of cross- platform and cross-lingual communication.
Additional outcomes generated during this project include the development of a system that uses deep learning on text and images to offline collective action using social media data, advances in knowledge of how the Chinese government uses social media for political propaganda and influence, and how people in very different contexts (US, China, Myanmar) use smartphones to consume media.
Last Modified: 11/09/2022
Modified by: Jennifer Pan
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