Computer Science

Social Clicks: What and Who Gets Read on Twitter?

April 13, 2016

Maksym Gabielkov, Arthi Ramachandran, Augustin Chaintreau, Arnaud Legout

Summary

The actual influence of an intermediary or a resource is poorly predicted by their share count.

Abstract

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:

  1. 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;
  2. Social media attention is actually long-lived, in contrast with temporal evolution estimated from shares or receptions;
  3. 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.

Journal

Proc. of ACM SIGMETRICS'16

Digital Object Identifier (DOI)

10.1145/2896377.2901462

Cite This Paper

Maksym Gabielkov, Arthi Ramachandran, Augustin Chaintreau, Arnaud Legout. Social Clicks: What and Who Gets Read on Twitter?. ACM SIGMETRICS / IFIP Performance 2016, Jun 2016, Antibes Juan-les-Pins, France. ⟨hal-01281190⟩

Citations

The following papers were conducted after this paper's publication, and reference this exact study. They can be thought of as 'ensuing from' or 'being derived from' this study.

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References

The following papers were cited within this study.

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