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AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism

arXiv 2407.12613 ↗First posted 17 Jul 2024cs.HC cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 17 Jul 2024.

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6 of 25 references.

References · 6

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  3. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  4. 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 417
  5. 2020Top2Vec: Distributed Representations of Topicscited by 126
  6. 2023FeedbackMap: a tool for making sense of open-ended survey responsescited by 4

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