CNN noticed that people were incredibly outspoken with election opinions on Facebook and wanted to make sense of this unorganized, yet extremely valuable information. However, with such great levels of volume and varying degrees of content consumability, CNN needed a way to digest and filter all of the chatter. CNN partnered with Mass Relevance and Facebook to transform Facebook posts into meaningful insights for their audience. Leveraging the Mass Relevance platform, CNN was able to aggregate, filter, and visualize real-time Facebook conversations about the presidential candidates. The Facebook + CNN Insights page incorporated a number of visualizations that allowed for a high degree of user adaptation. For example, users could view the election conversation by candidate, demographic, geography, or timeframe. Using Facebook content as the source fueling the experience, CNN could display raw mentions, but also provide insight into who was doing the talking.CNN successfully tapped into real-time social conversations to not only tell the news, but to transform social content into the news itself. The site also served as a great platform for political influencers to voice their opinions. By organizing Facebook posts by number of likes, the page served as a sort of online opinion meritocracy with the most-liked posts earning the top spots on the page. The feedback system prompted huge levels of engagement and allowed users to take part in meaningful debate. The CNN/Facebook page became a great place to access information as well as add to the discussion by posting or commenting. The page also helped CNN grow their social footprint. Users had the opportunity to like CNN Politics, helping them reach almost 100,00 fans by the end of the campaign. Even more impressive, over 43,000 people recommended the page on Facebook by clicking the unobtrusive recommend button on the top of the page. The campaign drove a total of 1.4 million visits. The top 5 posts on the page garnered an average of over 2500 likes, with the top pst capturing 7,500, well over the average of 54 likes for a typical post.
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