  Social influence pervades our everyday lives and lays the foundation for
complex social phenomena. In a crisis like the COVID-19 pandemic, social
influence can determine whether life-saving information is adopted. Existing
literature studying online social influence suffers from several drawbacks.
First, a disconnect appears between psychology approaches, which are generally
performed and tested in controlled lab experiments, and the quantitative
methods, which are usually data-driven and rely on network and event analysis.
The former are slow, expensive to deploy, and typically do not generalize well
to topical issues (such as an ongoing pandemic); the latter often oversimplify
the complexities of social influence and ignore psychosocial literature. This
work bridges this gap and presents three contributions towards modeling and
empirically quantifying online influence. The first contribution is a
data-driven Generalized Influence Model that incorporates two novel
psychosocial-inspired mechanisms: the conductance of the diffusion network and
the social capital distribution. The second contribution is a framework to
empirically rank users' social influence using a human-in-the-loop active
learning method combined with crowdsourced pairwise influence comparisons. We
build a human-labeled ground truth, calibrate our generalized influence model
and perform a large-scale evaluation of influence. We find that our generalized
model outperforms the current state-of-the-art approaches and corrects the
inherent biases introduced by the widely used follower count. As the third
contribution, we apply the influence model to discussions around COVID-19. We
quantify users' influence, and we tabulate it against their professions. We
find that the executives, media, and military are more influential than
pandemic-related experts such as life scientists and healthcare professionals.
