News organizations have vast archives of information, as well as a number of web analytic tools that aid in allocating editorial resources to cover different news events, and capitalize on this information. These tools allow editors and media managers to react to shifts in their audience’s interest, but what is lacking is a tool to help predict such shifts.

Qatar Computing Research Institute (QCRI) and Al Jazeera are announcing the launch of FAST (Forecast and Analytics of Social Media and Traffic), a platform that analyzes in real-time the life cycle of news stories on the web and social media, and provides predictive analytics that gauge audience interest.

Predicting user behavior around news articles is valuable for a news organization as it allows them to deliver more relevant and engaging content, as well as improve the allocation of resources to developing stories.

The study of consumption patterns of online news has attracted considerable attention from the research community for more than a decade, primarily making predictions on patterns as single time series to determine website traffic, number of visits, number of comments, and personalised news recommendations among others. Predicting user behaviour around news articles is valuable for a news organisation as it allows them to deliver more relevant and engaging content, as well as improve the allocation of resources to developing stories.

FAST introduces a unique approach to prediction by integrating different user interactions to a news article, including website visits, social media reactions, and search and referrals in order to forecast the number of page views an article will receive during its effective lifetime, which is approximately three days for most articles. This hybrid observation method is based on qualitative and quantitative analysis that determines typical patterns in the life cycle of news.

The platform accurately models the overall traffic an article will receive by observing the first 30 to 60 minutes of social media reactions. Achieving the same prediction accuracy by using data from visits alone would require at least three hours of data. FAST continuously learns to produce more accurate predictions as data from the most recent related articles streams into the system.

“One of the main conclusions from our research is that social media reactions cannot be ignored when producing traffic predictions,” said Dr. Carlos Castillo, Senior Scientist in QCRI’s Social Computing team. “You need to take into account not only the number of Facebook shares and tweets each article receives, but also the richness of the discussion around an article in Twitter. This leads to much more accurate predictions than simply extrapolating from current page views.”

Qatar Foundation corporate website.

QCRI is a national research institute and a member of Qatar Foundation Research and Development, supporting Qatar’s mission to build the nation’s innovation and technology capacity. Al Jazeera is an award-winning international news network headquartered in Doha, Qatar.

For further information:
Kimberly Mathern
QCRI Communications Manager
email: kmathern@qf.org.qa

Al Jazeera Media Network
email: pressoffice@aljazeera.net