skip to main content


Title: Algorithmic Journalism and Its Impacts on Work
In the artificial intelligence era, algorithmic journalists can produce news reports in natural language from structured data thanks to natural language generation (NLG) algorithms. This paper presents several algorithmic content generation models and discusses the impacts of algorithmic journalism on work within a framework consisting of three levels: replacing tasks of journalists, increasing efficiency, and developing new capabilities within journalism. The findings indicate that algorithmic journalism technology may lead some changes in journalism by enabling individual users to produce their own stories. This paper may contribute to an understanding of how algorithmic news is created and how algorithmic journalism technology impacts work.  more » « less
Award ID(s):
2026583
NSF-PAR ID:
10301595
Author(s) / Creator(s):
;
Date Published:
Journal Name:
C+J 2020 Symposium
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Environmental journalists, as gatekeepers, often become arbiters of risk and benefit information. This study explores how their routine news value judgments may influence reporting on marine aquaculture, a growing domestic industry with complex social and ecological impacts. We interviewed New England newspaper journalists using Q methodology, a qualitative dominant mixed-method approach to study shared subjectivity in small samples. Results revealed four distinct reporting perspectives—“state structuralist,” “neighborhood preservationist,” “industrial futurist,” and “local proceduralist”—stemming from the news value and objectivity routines journalists used in news selection. Findings suggest implications for public understanding of, and positionality toward, natural resource use and development. 
    more » « less
  2. null (Ed.)
    Concerns about the spread of misinformation online via news articles have led to the development of many tools and processes involving human annotation of their credibility. However, much is still unknown about how different people judge news credibility or the quality or reliability of news credibility ratings from populations of varying expertise. In this work, we consider credibility ratings from two “crowd” populations: 1) students within journalism or media programs, and 2) crowd workers on UpWork, and compare them with the ratings of two sets of experts: journalists and climate scientists, on a set of 50 climate-science articles. We find that both groups’ credibility ratings have higher correlation to journalism experts compared to the science experts, with 10-15 raters to achieve convergence. We also find that raters’ gender and political leaning impact their ratings. Among article genre of news/opinion/analysis and article source leaning of left/center/right, crowd ratings were more similar to experts respectively with opinion and strong left sources. 
    more » « less
  3. Concerns about the spread of misinformation online via news articles have led to the development of many tools and processes involving human annotation of their credibility. However, much is still unknown about how different people judge news credibility or the quality or reliability of news credibility ratings from populations of varying expertise. In this work, we consider credibility ratings from two “crowd” populations: 1) students within journalism or media programs, and 2) crowd workers on UpWork, and compare them with the ratings of two sets of experts: journalists and climate scientists, on a set of 50 climate-science articles. We find that both groups’ credibility ratings have higher correlation to journalism experts compared to the science experts, with 10-15 raters to achieve convergence. We also find that raters’ gender and political leaning impact their ratings. Among article genre of news/opinion/analysis and article source leaning of left/center/right, crowd ratings were more similar to experts respectively with opinion and strong left sources. 
    more » « less
  4. Espinosa-Anke, Luis ; Martín-Vide, Carlos ; Spasić, Irena (Ed.)
    Algorithmic journalism refers to automatic AI-constructed news stories. There have been successful commercial implementations for news stories in sports, weather, financial reporting and similar domains with highly structured, well defined tabular data sources. Other domains such as local reporting have not seen adoption of algorithmic journalism, and thus no automated reporting systems are available in these categories which can have important implications for the industry. In this paper, we demonstrate a novel approach for producing news stories on government legislative activity, an area that has not widely adopted algorithmic journalism. Our data source is state legislative proceedings, primarily the transcribed speeches and dialogue from floor sessions and committee hearings in US State legislatures. Specifically, we create a library of potential events called phenoms. We systematically analyze the transcripts for the presence of phenoms using a custom partial order planner. Each phenom, if present, contributes some natural language text to the generated article: either stating facts, quoting individuals or summarizing some aspect of the discussion. We evaluate two randomly chosen articles with a user study on Amazon Mechanical Turk with mostly Likert scale questions. Our results indicate a high degree of achievement for accuracy of facts and readability of final content with 13 of 22 users in the first article and 19 of 20 subjects of the second article agreeing or strongly agreeing that the articles included the most important facts of the hearings. Other results strengthen this finding in terms of accuracy, focus and writing quality. 
    more » « less
  5. As misinformation, disinformation, and conspiracy theories increase online, so does journalism coverage of these topics. This reporting is challenging, and journalists fill gaps in their expertise by utilizing external resources, including academic researchers. This paper discusses how journalists work with researchers to report on online misinformation. Through an ethnographic study of thirty collaborations, including participant-observation and interviews with journalists and researchers, we identify five types of collaborations and describe what motivates journalists to reach out to researchers — from a lack of access to data to support for understanding misinformation context. We highlight challenges within these collaborations, including misalignment in professional work practices, ethical guidelines, and reward structures. We end with a call to action for CHI researchers to attend to this intersection, develop ethical guidelines around supporting journalists with data at speed, and offer practical approaches for researchers filling a “data mediator” role between social media and journalists. 
    more » « less