AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910696795537408 |
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| author | Brannon, William Beeferman, Doug Jiang, Hang Heyward, Andrew Roy, Deb |
| author_facet | Brannon, William Beeferman, Doug Jiang, Hang Heyward, Andrew Roy, Deb |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12613 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism Brannon, William Beeferman, Doug Jiang, Hang Heyward, Andrew Roy, Deb Human-Computer Interaction Computation and Language 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. |
| title | AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2407.12613 |