Today’s newspaper publishers face challenges that would never even have occurred to their predecessors. Indeed, in today’s world of online publishing, editors must constantly monitor the traffic on their newspaper sites and make quick decisions about the content of these publications. Promotional platforms such as search engines and social media make the task all the more complex: an editor is faced with the challenge of taking in a vast amount of information in order to predict exactly how a piece of content will affect website traffic and, thereby, deciding which articles should be promoted from one minute to the next. Even by publishing standards, this is a daunting task. Like many publishers, News UK, the publisher of the Sun, the Times, and the Sunday Times, found that this posed a major challenge and made a decision to use data to its advantage, choosing to focus on analyzing article content.
Daniel Gilbert and Jonathan Leslie discuss an ongoing partnership between News UK and Pivigo in which teams of data scientists in training embarked on five-week data science projects aimed at using advanced analytics and machine learning methodologies to generate insight and help News UK make better-informed decisions. To better understand how differences in content affect article performance, one team examined what determines the lifetime of an article (i.e., what factors determine how much traffic an article generates and what the trajectory of that article’s popularity might look like over time), helping the News UK editors make accurate predictions about the traffic pattern produced by a given article and could, therefore, make better-informed decisions about which articles to promote on social media. To complement this work, a second team investigated how an article’s content affects its popularity and how different types of articles respond to promotion on social media and used their research to create a bespoke analytics tool that editors could use to make accurate predictions about which articles might respond best to promotion on social media and which social media platform would likely produce the greatest effect.
During these studies, it occurred to the data team at News UK that they periodically observed deviations from normal patterns of website traffic. However, traditional analytics tools were cumbersome to operate and fell short in explaining these deviations in terms that a nonspecialist could use. A third team of Pivigo data scientist trainees built an anomaly detection system designed to examine time series data generated from digital clickstreams, identify abnormal trends, and explain anomalies using natural language. Early tests of this system showed that it could capture anomalous spikes in traffic due to atypical popularity of some content, allowing editors to quickly understand which topics were trending at higher-than-expected levels. Additionally, the team also found that this tool has another unexpected function: it can flag errors in the data tracking in a matter of seconds, often before any built-in warnings could respond. This system gives the editorial team at News UK a powerful tool that can help them to quickly understand the impact of their content and make decisions in a far more agile way than was previously possible.
Today the outputs of the three projects are making their way into common usage across the newsrooms alongside other algorithms developed in-house by the much larger data science team and are helping complement the expertise of editors and journalists and aid decision making. This work underscores the power of data science and shows how even a small team of data scientists can bring real value to a large, data-rich organization in a short period of time.
Daniel Gilbert is director of data at News UK. Previously, Daniel was a data science consultant working with publishers in the UK and US.
Jonathan Leslie is head of data science at Pivigo, where he works with business partners to develop data science solutions that make the most of their data, including in-depth analysis of existing data and predictive analytics for future business needs. He also programs, mentors, and manages teams of data scientists on projects in a wide variety of business domains.
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