Automated news in practice: a cross-national exploratory study

Area of Science:

• Investigates the evolving landscape of automated journalism, focusing on text generation via software and algorithms.

Background:

• Examines the current state and future trajectory of automated news generation across 18 news organizations in Europe, North America, and Australia.
• Employs a strategic sampling method based on Hallin and Mancini's media system typology for cross-national analysis.

Discussion:

• Utilizes semi-structured interviews with editorial staff, executives, and technologists.
• Applies Actor-Network Theory (ANT) to understand agency in automated news processes, identifying key 'interferences' in linear situations.

Key Insights:

• Identifies primary interferences in automated news: alternative data sources (internal feeds, crowdsourced material), in-house interfaces for journalistic participation (self-editing tools, notification streams), and non-textual outputs (audio summaries).
• Findings indicate increased journalistic professionalization due to these advancements.

Outlook:

• Highlights the potential for news organizations to become overly dependent on Big Tech for data and dissemination of automated news.
• Suggests that mutual negotiations and re-aligned interests may emerge as platforms confront journalistic challenges.