CIP awards 2021 Innovation Fund grants to 4 projects

Dec 17, 2021

This fall, the Center for an Informed Public awarded Innovation Fund grants to four project proposals, funding that will help support collaborative, multi-disciplinary and timely work intended to advance the CIP’s mission to resist strategic misinformation, promote an informed society and strengthen democratic discourse. The Innovation Fund is intended to seed promising new ideas and generate proofs-of-concept for further development. Award amounts average around $10,000 and project periods are 6-12 months.

The CIP’s Innovation Fund grants for 2021 wouldn’t be possible without the generous financial support of the John S. and James L. Knight Foundation and the University of Washington’s Technology & Social Change Group (TASCHA).

The four projects awarded funding are detailed below:

How Domestic Divisions Shape Perceptions of Foreign Disinformation 

  • Scott Radnitz, Associate Professor, Jackson School of International Studies
  • Yuan Hsiao, Assistant Professor, Department of Communication

Summary: This project seeks to assess how the partisan framing of claims about foreign disinformation affects the public’s belief in the accuracy of the information, willingness to share it on social media, and trust in political institutions. Given the increasing public awareness about Russian and Chinese influence in disinformation on social media, this project plans to conduct survey experiments in three countries — Ukraine, Taiwan, and the U.S. — targeted in Russia and China’s disinformation campaigns. This work will fill in the gap of the spread of disinformation in polarized societies, focusing on the intersection of domestic politics and foreign influence.

Sending News Back Home: Analyzing the Spread of Misinformation between Vietnam and Diasporic Communities in the 2020 U.S. Election

  • Rachel E. Moran, CIP Postdoctoral Fellow

Summary: The overall objective of this ongoing project is to better understand how misinformation flows across diaspora and in non-English language communities. The first part of the project has analyzed the spread of misinformation on social media related to the 2020 U.S. election and conducted focus groups with Vietnamese Americans to explore the impact of misinformation. We now aim to facilitate workshops with diverse groups in the Vietnamese American community, gather feedback, and translate research findings into formats that can better inform the community.

A Tool to Support Peer Discussions Towards Correcting Misinformation On Social Media

  • Amy X. Zhang, Assistant Professor, Paul G. Allen School of Computer Science & Engineering
  • Franziska Roesner, Associate Professor, Paul G. Allen School of Computer Science & Engineering

Summary: This project aims to design an embedded tool on Twitter to guide users through the process of debunking mis- and disinformation in an online discussion, from identifying the topic to crafting a response. Through a mix-method of data collection, lab study, and field study, we will design and evaluate the tool for continuous improvement, making sure it will help people engage in productive discussions around misinformation and correcting it. This work will contribute to our broader research efforts to empower everyday people to combat misinformation and access credible information online.

Supporting Machine-assisted Crowd Expert Credibility Assessment of Search Results

  • Tanu Mitra, Assistant Professor, Information School

Summary: This project aims to answer the following questions: How can we scale and sustain fact-checking online, without compromising the quality of fact-checks? How can we move towards technology-enabled human-centric fact-checking that supports and sustains fact-checkers’ professional values and code of principles? Through interviews, observation, and surveys, we will draw insights to understand fact-checkers values, principles, and roles in the fact-checking process. Furthermore, to ensure adherence to fact-checking values, we plan to develop a mixed-initiative annotation system that combines human intelligence with automated algorithmic systems to offer high-quality and scalable credibility assessments online.

 

 

 

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