AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism
Author(s)
Brannon, William; Beeferman, Doug; Jiang, Hang; Heyward, Andrew; Roy, Deb
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Understanding and making use of audience feedback is important but difficult for journalists, who now face an impractically large volume of audience comments online. We introduce AudienceView, an online tool to help journalists categorize and interpret this feedback by leveraging large language models (LLMs). AudienceView identifies themes and topics, connects them back to specific comments, provides ways to visualize the sentiment and distribution of the comments, and helps users develop ideas for subsequent reporting projects. We consider how such tools can be useful in a journalist's workflow, and emphasize the importance of contextual awareness and human judgment.
Description
CSCW Companion ’24, November 9–13, 2024, San Jose, Costa Rica
Date issued
2024-11-11Department
Massachusetts Institute of Technology. Media LaboratoryPublisher
ACM|Companion of the 2024 Computer-Supported Cooperative Work and Social Computing
Citation
Brannon, William, Beeferman, Doug, Jiang, Hang, Heyward, Andrew and Roy, Deb. 2024. "AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism."
Version: Final published version
ISBN
979-8-4007-1114-5
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