Rebecca Hawkes | 05-08-2013
A new platform analysing the life cycle of Internet news stories is being launched by Qatar Computing Research Institute (QCRI) and television news network Al Jazeera to help predict user behaviour.
The platform, FAST (Forecast and Analytics of Social Media and Traffic), will, say the developers, allow news organisations to deliver more relevant and engaging content as well as improve the allocation of resources to developing stories.
"Al Jazeera English's website thrives on good original content in news and features, dynamic ways of creativity through interactive and crowd sourcing methods, and up-to-date social media tools," said Imad Musa, head of online, Al Jazeera English.
"[FAST] allows us to understand the consumption of news and what is expected to do well in driving traffic forward. Analytics in predicting the future trend of a Web story is a crucial component in understanding Web traffic."
The QCRI and Al Jazeera research partnership has "developed a platform that will help shift the way media does business," said Dr Ahmed Elmagarmid, executive director, QCRI.
Using a hybrid observation method, FAST prediction integrates 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 around three days for the majority of articles.
The underlying algorithms, the result of research at QCRI, Al Jazeera, Carnegie Mellon University and the MIT Center for Civic Media, have been validated using data made available by Al Jazeera English. The platform accurately models the overall traffic an article will receive by observing the first 30-60 minutes of social media reactions, says QCRI part of Qatar Foundation.
"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."