2,178 research outputs found
Scalable Methods for Adaptively Seeding a Social Network
In recent years, social networking platforms have developed into
extraordinary channels for spreading and consuming information. Along with the
rise of such infrastructure, there is continuous progress on techniques for
spreading information effectively through influential users. In many
applications, one is restricted to select influencers from a set of users who
engaged with the topic being promoted, and due to the structure of social
networks, these users often rank low in terms of their influence potential. An
alternative approach one can consider is an adaptive method which selects users
in a manner which targets their influential neighbors. The advantage of such an
approach is that it leverages the friendship paradox in social networks: while
users are often not influential, they often know someone who is.
Despite the various complexities in such optimization problems, we show that
scalable adaptive seeding is achievable. In particular, we develop algorithms
for linear influence models with provable approximation guarantees that can be
gracefully parallelized. To show the effectiveness of our methods we collected
data from various verticals social network users follow. For each vertical, we
collected data on the users who responded to a certain post as well as their
neighbors, and applied our methods on this data. Our experiments show that
adaptive seeding is scalable, and importantly, that it obtains dramatic
improvements over standard approaches of information dissemination.Comment: Full version of the paper appearing in WWW 201
Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness
Youth experiencing homelessness (YEH) are subject to substantially greater
risk of HIV infection, compounded both by their lack of access to stable
housing and the disproportionate representation of youth of marginalized
racial, ethnic, and gender identity groups among YEH. A key goal for health
equity is to improve adoption of protective behaviors in this population. One
promising strategy for intervention is to recruit peer leaders from the
population of YEH to promote behaviors such as condom usage and regular HIV
testing to their social contacts. This raises a computational question: which
youth should be selected as peer leaders to maximize the overall impact of the
intervention? We developed an artificial intelligence system to optimize such
social network interventions in a community health setting. We conducted a
clinical trial enrolling 713 YEH at drop-in centers in a large US city. The
clinical trial compared interventions planned with the algorithm to those where
the highest-degree nodes in the youths' social network were recruited as peer
leaders (the standard method in public health) and to an observation-only
control group. Results from the clinical trial show that youth in the AI group
experience statistically significant reductions in key risk behaviors for HIV
transmission, while those in the other groups do not. This provides, to our
knowledge, the first empirical validation of the usage of AI methods to
optimize social network interventions for health. We conclude by discussing
lessons learned over the course of the project which may inform future attempts
to use AI in community-level interventions
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