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.
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.
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