| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | December 20, 2022 |
| Latest Amendment Date: | December 20, 2022 |
| Award Number: | 2309846 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Ruth Shuman
rshuman@nsf.gov (703)292-2160 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | December 1, 2022 |
| End Date: | October 31, 2024 (Estimated) |
| Total Intended Award Amount: | $50,000.00 |
| Total Awarded Amount to Date: | $38,515.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4300 MARTIN LUTHER KING BLVD HOUSTON TX US 77204-3067 (713)743-5773 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4800 W Calhoun Houston TX US 77204-3067 |
| 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): | I-Corps |
| 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): | 47.084 |
ABSTRACT
The broader impact/commercial potential of this I-Corps project is the development of an online dashboard with misinformation forecast trends and analysis to help address the misinformation endemic in America. Misinformation is a significant issue that may create confusion and misunderstanding about essential topics. Misinformation online may result in people questioning evidence-based medical guidance or refusing safe treatments. Understanding current misinformation content and trends supports both corporate entities and social media users. For corporations, th proposed suite of tools may show how misinformation impacts businesses by exploring and forecasting public sentiment concerning relevant misinformation topics. This analysis may provide value to these agencies by shifting resources from manual identification to analyzing the content and implications of misinformation posts. Individual users may be better equipped by understanding not only the major topics in misinformation but also an explanation of why this misinformation topic is being spread. Potential customers may be drawn to this proposed technology since information demand is met from grassroots organizations, which can be inconsistent with data quality.
This I-Corps project is based on the development of automated data collection, data analytics, and deep learning methodologies. The goal is to develop an application and associated website that centralizes up-to-date misinformation content and metrics. Such a solution will provide potential customers with an enterprise-level system to help better understand the implications and types of misinformation spread across social media platforms. Currently, an array of tools to interact with misinformation content is under development. Pipelines are being constructed to channel and store raw data using the selenium framework for the collection step. Thesedata are stored locally and will act as the raw information to support the finalized dashboard. Deep learning models that are trained to identify text-based misinformation have been developed with the goal of expanding to image, sound, and video identification to address current social content trends. This Transformers framework also identifies sentiment on specified topic groups to support analysis. The proposed technology involves multiple research areas, including big data, natural language processing, artificial intelligence, and statistical analysis.
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.
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