AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism
Name
3678884.3681821.pdf
Size
1.54 MB
Format
Adobe PDF
Checksum (MD5)
30d14a84b9c2956c28559b627b53708e
Author(s)
Brannon, William
Beeferman, Doug
Jiang, Hang
Heyward, Andrew
Roy, Deb
Date Issued
November 11, 2024
Publisher
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
Abstract
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
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3678884.3681821
