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Al Jazeera platform to guage audience's interest in news
 
TRIBUNE NEWS NETWORK

DOHA QATAR Computing Research Institute (QCRI) and Al Jazeera have launched a platform that analyses the life cycle of news stories on the web and social media, and provides predictive analytics that gauge audience interest.

The platform is called Forecast and Analytics of Social Media and Traffic (FAST).

Executive Director of QCRI Dr Ahmed Elmagarmid said, œThe explosion of big data in the media domain has provided QCRI an excellent research opportunity to develop an innovative way to derive value from the information. Together with our valued partner, Al Jazeera, the QCRI team has developed a platform that will help shift the way media does business.  Head of Online for Al Jazeera English Imad Musa said, œAl 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. We welcome working with QCRI in developing FAST as it 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.

This initiative is a component we welcome.  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. The hybrid observation method is based on qualitative and quantitative analysis that determines typical patterns in the life cycle of news.

The underlying algorithms, which are the result of joint research by scientists at QCRI, Al Jazeera, Carnegie Mellon University and the MIT Center for Civic Media, have been validated using vast amounts of data made available by Al Jazeera English.

The platform accurately models the overall traffic an article will receive by observing the first thirty to sixty 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 learns to produce more accurate predictions as data from the most recent related articles streams into the system.

Senior Scientist in QCRI's Social Computing team Dr Carlos Castillo said, œOne of the main conclusions from our research is that social media reactions cannot be ignored when producing traffic predictions. 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.  More information about FAST is available on http://fast.qcri.org/.


 
 

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