The tiny fraction of headlines that news editors push out on Twitter draw a large share of eyeballs, but it’s the stories recommended by friends that trigger more clicks.
In what may be the first independent study of news consumption on social media, researchers at Columbia University and the French National Institute (Inria) found that reader referrals drove 61 percent of the nearly 10 million clicks in a random sample of news stories posted on Twitter. The researchers present their results on June 16 at the Association for Computing Machinery’s Sigmetrics conference in Nice.
“Readers know best what their followers want,” said the study’s senior author, Augustin Chaintreau, a computer science professor at the Data Science Institute and Columbia Engineering. “In the future, they will have more and more say about what’s newsworthy.”
For those willing to read, the study finds that stories on Twitter have a relatively long shelf life. While more than 90 percent of links in the study were shared within a few hours, most links were clicked on, and presumably read, much later; 70 percent of clicks happened after the first hour, and a full 18 percent happened in the second week, the study found.
“Our results show that sharing content and actually reading it are poorly correlated,” said [study coauthor Arnaud] Legout. “Likes and shares are not a meaningful measure of content popularity. This means that the industry standard for popularity needs to be rethought.”
Read the Complete Research Summary
Read the Full Text Conference Paper: “Social Clicks: What and Who Gets Read on Twitter?” (15 pages; PDF)
Online news domains increasingly rely on social media to drive traffic to their websites. Yet we know surprisingly little about how a social media conversation mentioning an online article actually generates clicks. Sharing behaviors, in contrast, have been fully or partially available and scrutinized over the years. While this has led to multiple assumptions on the diffusion of information, each assumption was designed or validated while ignoring actual clicks. We present a large scale, unbiased study of social clicks – that is also the first data of its kind – gathering a month of web visits to online resources that are located in 5 leading news domains and that are mentioned in the third largest social media by web referral (Twitter). Our dataset amounts to 2.8 million shares, together responsible for 75 billion potential views on this social media, and 9.6 million actual clicks to 59,088 unique resources. We design a reproducible methodology and carefully correct its biases. As we prove, properties of clicks impact multiple aspects of information diffusion, all previously unknown. (i) Secondary resources, that are not promoted through headlines and are responsible for the long tail of content popularity, generate more clicks both in absolute and relative terms. (ii) Social media attention is actually long-lived, in contrast with temporal evolution estimated from shares or receptions. (iii) The actual influence of an intermediary or a resource is poorly predicted by their share count, but we show how that prediction can be made more precise.
Paper Presented At:
ACM SIGMETRICS / IFIP Performance 2016, Jun 2016, Antibes Juan-les-Pins, France. 2016