EarlyBird: Mobile Prefetching of Social Network Feeds via Content Preference Mining and Usage Pattern Analysis

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 identified from our dataset, we present inference-based social network content prefetcher, EarlyBird. It uses the specific 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.