[Colloq] Arnaud Legout (11am tomorrow, 366WVH)

[Dave Choffnes]

Arnaud Legout from Inria will be visiting tomorrow and giving a talk.
Please attend, and if you want to meet 1-on-1, sign up here:
https://wiki.ccs.neu.edu/display/VISCHED/Arnaud+Legout+%28Inria%29+-+July+26th

Talk details below:

*Speaker:* Arnaud Legout, Inria Sophia Antipolis, France

*Title:* Social Clicks: What and Who Gets Read on Twitter?

*Abstract:*

Online news domains increasingly rely on social media to drive traffic to
their website. Yet we know surprisingly little about how social media
conversation mentioning an online article actually generates a click to it.
Posting behaviors, in contrast, have been fully or partially available and
scrutinized over the years. While this has led to to multiple assumptions
on the diffusion of information, each were designed or validated while
ignoring this important step.

In this talk, we present a large scale, validated and reproducible 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 posts, 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, carefully corrected its biases, enabling data sharing, future
collection and validation. As we prove, properties of clicks and social
media Click-Through-Rates (CTR) impact multiple aspects of information
diffusion, all previously unknown. 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.
Social media attention is actually long-lived, in contrast with temporal
evolution estimated from posts or impressions. The actual influence of an
intermediary or a resource is poorly predicted by their posting behavior,
but we show how that prediction can be made more precise.