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document.write(' 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.
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document.write(' 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.
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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. ');
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