Deepnews.ai has a simple and accessible scoring system: the online platform receives a batch of news stories will score on a scale of 1 to 5 based on their journalistic quality. This is done automatically and in real time. This scoring system has multiple applications.
On the business side, the greatest potential is the possibility to adjust the price of an advertisement to the quality of the editorial context. There is room for improvement. Today, a story that required months of work and cost hundreds of thousands of dollars carries the same unitary value (a few dollars per thousand page views) as a short, gossipy article. But times are changing. In the digital ad business, indicators are blinking red: CPMs, click-through rates, and viewability are on a steady downward decline. We believe that inevitably, advertisers and marketers will seek high-quality content—as long as they can rely on a credible indicator of quality. Deepnews.ai will interface with ad servers to assess the value of a story and price and serve ads accordingly. The higher a story’s quality score, the pricier the ad space adjacent to it can be. This adjustment will substantially raise the revenue per page to match the quality of news.
On the editorial side: The ability to assess the quality of news will open up opportunities for new products and services such as:
Using traditional ML methods, the first model assesses quality taking as input two sets of “signals” to assess the quality of journalistic work: Quantifiable Signals and Subjective Signals. Quantifiable 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). This is processed data from news content. Subjective Signals are human scoring of quality based on criteria such as writing style, thoroughness, balance and fairness, timeliness, etc. These measures are produced by editors and experienced journalists.
The second approach is based on deep learning methods. Here, we aim to build models that will be able to accurately classify an unseen incoming article purely based on the quality of the report, distinct from the metadata or the topic of discussion. The main challenge in many such deep learning approaches is the availability of labeled data. We processed nearly four million contemporary articles coming from sources deemed as “good” or “commodity” (with no journalistic value-added). For the bulk of our data, we rely on the reputation and consistency of the brand of the news provider but we aim to be able to classify quality at a finer grained level, detached from the name of the source. To this end, we also experiment with various models to capture quality difference in writing that is agnostic to topical differences. Frederic Filloux (filloux@stanford.edu) Eun Seo Jo (eunseo@stanford.edu)