Understanding Practices around Computational News Discovery Tools in the
Domain of Science Journalism
- URL: http://arxiv.org/abs/2311.06864v2
- Date: Tue, 28 Nov 2023 16:47:49 GMT
- Title: Understanding Practices around Computational News Discovery Tools in the
Domain of Science Journalism
- Authors: Sachita Nishal, Jasmine Sinchai, Nicholas Diakopoulos
- Abstract summary: We explore computational methods to aid these journalists' news discovery in terms of time-efficiency and agency.
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.
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.
- Score: 3.660182910533372
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources [100.23208165760114]
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications.
To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet.
arXiv Detail & Related papers (2024-06-24T15:55:49Z) - SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generation [20.994565065595232]
We present a new corpus to facilitate the automated generation of scientific news reports.
Our dataset comprises academic publications and their corresponding scientific news reports across nine disciplines.
We benchmark our dataset employing state-of-the-art text generation models.
arXiv Detail & Related papers (2024-03-26T14:54:48Z) - Envisioning the Applications and Implications of Generative AI for News
Media [4.324021238526106]
This article considers the increasing use of algorithmic decision-support systems and synthetic media in the newsroom.
We draw from a taxonomy of tasks associated with news production, and discuss where generative models could appropriately support reporters.
Our essay is relevant to practitioners and researchers as they consider using generative AI systems to support different tasks.
arXiv Detail & Related papers (2024-02-29T03:40:25Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Identifying Informational Sources in News Articles [109.70475599552523]
We build the largest and widest-ranging annotated dataset of informational sources used in news writing.
We introduce a novel task, source prediction, to study the compositionality of sources in news articles.
arXiv Detail & Related papers (2023-05-24T08:56:35Z) - LLM-based Interaction for Content Generation: A Case Study on the
Perception of Employees in an IT department [85.1523466539595]
This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company.
Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be.
Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
arXiv Detail & Related papers (2023-04-18T15:35:43Z) - Understanding Journalists' Workflows in News Curation [10.16152286476502]
We interviewed journalists who curate newsletters from around the world.
Our findings lay out the role of journalists' prior experience in the value they bring into the curation process.
We highlight the importance of hybrid curation and provide design insights on how technology can support the work of these experts.
arXiv Detail & Related papers (2023-03-31T21:16:22Z) - Modeling Information Change in Science Communication with Semantically
Matched Paraphrases [50.67030449927206]
SPICED is the first paraphrase dataset of scientific findings annotated for degree of information change.
SPICED contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers.
Models trained on SPICED improve downstream performance on evidence retrieval for fact checking of real-world scientific claims.
arXiv Detail & Related papers (2022-10-24T07:44:38Z) - CitationIE: Leveraging the Citation Graph for Scientific Information
Extraction [89.33938657493765]
We use the citation graph of referential links between citing and cited papers.
We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
arXiv Detail & Related papers (2021-06-03T03:00:12Z) - Generating Knowledge Graphs by Employing Natural Language Processing and
Machine Learning Techniques within the Scholarly Domain [1.9004296236396943]
We present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications.
Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools.
We generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain.
arXiv Detail & Related papers (2020-10-28T08:31:40Z) - The Right Tools for the Job: The Case for Spatial Science Tool-Building [0.0]
This paper presents the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC.
It discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this.
The paper concludes with paths forward, emphasizing open-source software and reusable computational data science.
arXiv Detail & Related papers (2020-08-12T20:15:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.