Social networks are the most engaging applications on mobile
devices, and they are becoming the main sources for
users to consume content. However, content retrieval, especially
for embedded links and multimedia, can often be too
slow, too energy hungry or too expensive for on-the-go mobile
users. To address these issues, we collect and analyze
a large set of traces from over 6000 real-life users of a popular
mobile Twitter client. Based on the unique challenges
identied from our dataset, we present inference-based social
network content prefetcher, EarlyBird. It uses the speci
c signals unique to social data in order to retrieve news
feeds and associated links and multimedia ahead of users’ usage.
Our regression-based content prediction model is able
to estimate a user’s likely content interests 55% of the time.
Second, we develop a prefetch scheduling scheme to maximize
delay reduction under users’ resource constraints. For
validation, we apply Earlybird to our collected dataset. We
show that on average users can reduce their delays by 62 %
at the cost of no more than 3% battery and 40MB/month
cellular data.