# Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism

arXiv: https://arxiv.org/abs/2311.06864 · HTML: https://mlsanity.com/paper/2311.06864/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 128 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 20 of 20)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://mlsanity.com/paper/2209.07858/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [RankME: Reliable Human Ratings for Natural Language Generation](https://mlsanity.com/paper/1803.05928/) (2018)
- [The Challenge of Crafting Intelligible Intelligence](https://mlsanity.com/paper/1803.04263/) (2018)
- [Supporting Human-AI Collaboration in Auditing LLMs with LLMs](https://mlsanity.com/paper/2304.09991/) (2023)
- [Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice](https://mlsanity.com/paper/2210.03709/) (2022)
- [Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions](https://mlsanity.com/paper/2103.01774/) (2021)
- [A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations](https://mlsanity.com/paper/1704.05150/) (2017)
- [Enough With "Human-AI Collaboration"](https://mlsanity.com/paper/2306.01615/) (2023)
- [Supporting the use of user generated content in journalistic practice](https://mlsanity.com/paper/1702.06491/) (2017)
- [Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data](https://mlsanity.com/paper/1711.04068/) (2017)
- [The Explanatory Gap in Algorithmic News Curation](https://mlsanity.com/paper/2109.15224/) (2021)

## Built on by (most cited first, 7 of 7)

- [Measuring and mitigating overreliance to build human-compatible AI](https://mlsanity.com/paper/2509.08010/) (2025)
- [Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research](https://mlsanity.com/paper/2404.05213/) (2024)
- ["Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism](https://mlsanity.com/paper/2501.17299/) (2025)
- ["It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry](https://mlsanity.com/paper/2409.12000/) (2024)
- [Using Generative Agents to Create Tip Sheets for Investigative Data Reporting](https://mlsanity.com/paper/2409.07286/) (2024)
- [RevTogether: Supporting Science Story Revision with Multiple AI Agents](https://mlsanity.com/paper/2503.01608/) (2025)
- [De-jargonizing Science for Journalists with GPT-4: A Pilot Study](https://mlsanity.com/paper/2410.12069/) (2024)

## Abstract (arXiv)

Science and technology journalists today face challenges in finding newsworthy leads due to increased workloads, reduced resources, and expanding scientific publishing ecosystems. Given this context, we explore computational methods to aid these journalists' news discovery in terms of time-efficiency and agency. In particular, we prototyped three computational information subsidies into an interactive tool that we used as a probe to better understand how such a tool may offer utility or more broadly shape the practices of professional science journalists. Our findings highlight central considerations around science journalists' agency, context, and responsibilities that such tools can influence and could account for in design. Based on this, we suggest design opportunities for greater and longer-term user agency; incorporating contextual, personal and collaborative notions of newsworthiness; and leveraging flexible interfaces and generative models. Overall, our findings contribute a richer view of the sociotechnical system around computational news discovery tools, and suggest ways to improve such tools to better support the practices of science journalists.