A batch of stories will be submitted to the platform and they will be scored on a scale of 1 to 5 based on their journalistic quality. This process will be performed automatically and in real time. They define quality narrowly; in its simplest terms, they look for value-added journalism. This means coverage built on a genuine journalistic approach: depth of reporting, expertise, investigation, analysis, ethical processes, and resources deployed by the newsroom. The platform is based on a combination of two models. — The first model involves two sets of “signals” to assess the quality of journalistic work: Quantifiable Signals and Subjective Signals. Quantifiable Signals are collected automatically. These signals include the structure and patterns of the HTML page, advertising density, use of visual elements, bylines, word count, readability of the text, information density (number of quotes and named entities). Subjective Signals are based on criteria used by editors (and intuitively by readers) to assess the quality of a story: writing style, thoroughness, balance & fairness, timeliness, etc. (This set will be used only in the building phase of the model). The second model is based on deep learning techniques, like “text-embedding” in which texts from large volumes of data (millions of articles) are converted into numerical values to be fed into a neural network. This neural net returns probabilities of scoring. This will be added to Journalism Resources Subject Tracer™. This will be added to Information Quality Resources Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.