  Detection of vaccine adverse events is crucial to the discovery and
improvement of problematic vaccines. To achieve it, traditionally formal
reporting systems like VAERS support accurate but delayed surveillance, while
recently social media have been mined for timely but noisy observations.
Utilizing the complementary strengths of these two domains to boost the
detection performance looks good but cannot be effectively achieved by existing
methods due to significant differences between their data characteristics,
including: 1) formal language v.s. informal language, 2) single-message per
user v.s. multi-messages per user, and 3) one class v.s. binary class. In this
paper, we propose a novel generic framework named Multi-instance Domain
Adaptation (MIDA) to maximize the synergy between these two domains in the
vaccine adverse event detection task for social media users. Specifically, we
propose a generalized Maximum Mean Discrepancy (MMD) criterion to measure the
semantic distances between the heterogeneous messages from these two domains in
their shared latent semantic space. Then these message-level generalized MMD
distances are synthesized by newly proposed mixed instance kernels to
user-level distances. We finally minimize the distances between the samples of
the partially-matched classes from these two domains. In order to solve the
non-convex optimization problem, an efficient Alternating Direction Method of
Multipliers (ADMM) based algorithm combined with the Convex-Concave Procedure
(CCP) is developed to optimize parameters accurately. Extensive experiments
demonstrated that our model outperformed the baselines by a large margin under
six metrics. Case studies showed that formal reports and extracted
adverse-relevant tweets by MIDA shared a similarity of keyword and description
patterns.
