  Facebook News Feed personalization algorithm has a significant impact, on a
daily basis, on the lifestyle, mood and opinion of millions of Internet users.
Nonetheless, the behavior of such algorithm lacks transparency, motivating
measurements, modeling and analysis in order to understand and improve its
properties. In this paper, we propose a reproducible methodology encompassing
measurements, an analytical model and a fairness-based News Feed design. The
model leverages the versatility and analytical tractability of time-to-live
(TTL) counters to capture the visibility and occupancy of publishers over a
News Feed. Measurements are used to parameterize and to validate the expressive
power of the proposed model. Then, we conduct a what-if analysis to assess the
visibility and occupancy bias incurred by users against a baseline derived from
the model. Our results indicate that a significant bias exists and it is more
prominent at the top position of the News Feed. In addition, we find that the
bias is non-negligible even for users that are deliberately set as neutral with
respect to their political views, motivating the proposal of a novel and more
transparent fairness-based News Feed design.
