1. Introduction

Journalism is in trouble and faces severe problems. Never before have we had so much information and so little information-based reporting. Investigative journalism is becoming scarce in journalists and outlets. Local newsrooms are becoming desolate. Public trust in the media has been dangerously eroded. Media is often a blaring confirmation bias horn. Internet traffic has become the primary business model — truth, honesty, and fairness are now nice-to-haves, prioritized by a shrinking pool of organizations. Journalists are increasingly being used as pawns by government and private actors.

While all of this is happening, technology quickly passes journalism behind. Instead of using computers to simply "spice up" or distribute a story, we are exploring the ways computers can be used to fundamentally improve the journalistic process.

We look to explore questions like the following:

  • Can we use algorithms to find leads that otherwise would have been overlooked or unpursued due to bias?

  • Can we use fraud-detection techniques to identify political misconduct?

  • Can we use data mining techniques to identify the conflicts of interest and collusion that exist within our government more easily?

  • Can we use clustering and text summarization to help journalists become more efficient and effective?

2. Submission Guidelines

We’re looking for papers that explore applications of modern advances in algorithms/computing to investigative reporting. In general, we’re looking for ideas along the lines of:

  • Practical applications of machine learning techniques/algorithms to journalism/reporting.

  • New ways to consolidate, find, or otherwise make available datasets with journalistic applications.

  • Dissections of stories generated directly by machine learning.

  • Any general machine learning, data mining, or computational topic that has relevance in journalism, such as text mining, outlier detection, document clustering, dimensionality reduction, text vectorization, graph theory, etc.

  • Meta essays or discussions of the intersection of journalism, The Media, and algorithms/technology.

Make sure your submission contains the essentials: manuscript, author(s), abstract (if applicable), keywords, citations. Please spell and grammar check your work prior to submission. English-language only. Documents should be formatted as PDF, Word DOCX, Markdown or AsciiDoc. If you have any formulas/charts you can attach them as images (inline or separated/referenced) or as LaTeX code. All documents will be converted to PDF during the review process.

If there is a funding source, please describe the organization’s role in the research/analysis, if any.

All submissions should be sent to: <artificialinformer@nym.hush.com>

3. Who are we?

We are a group of machine learning engineers, academics, and journalists looking to improve the media using modern computational techniques. If you feel you could help us with submissions, review, etc., then please get in touch.

4. Mailing List

If you want to be notified of publication releases, sign up here:

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5. Contact

General inquiries, questions, or submissions can be sent to the following email address: <artificialinformer@nym.hush.com>.

5.1. PGP

For those who want to send encrypted email, our public key follows. It will change from time-to-time so always have the latest.

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