  Nutrition is a key determinant of long-term health, and social influence has
long been theorized to be a key determinant of nutrition. It has been difficult
to quantify the postulated role of social influence on nutrition using
traditional methods such as surveys, due to the typically small scale and short
duration of studies. To overcome these limitations, we leverage a novel source
of data: logs of 38 million food purchases made over an 8-year period on the
Ecole Polytechnique Federale de Lausanne (EPFL) university campus, linked to
anonymized individuals via the smartcards used to make on-campus purchases. In
a longitudinal observational study, we ask: How is a person's food choice
affected by eating with someone else whose own food choice is healthy vs.
unhealthy? To estimate causal effects from the passively observed log data, we
control confounds in a matched quasi-experimental design: we identify focal
users who at first do not have any regular eating partners but then start
eating with a fixed partner regularly, and we match focal users into comparison
pairs such that paired users are nearly identical with respect to covariates
measured before acquiring the partner, where the two focal users' new eating
partners diverge in the healthiness of their respective food choice. A
difference-in-differences analysis of the paired data yields clear evidence of
social influence: focal users acquiring a healthy-eating partner change their
habits significantly more toward healthy foods than focal users acquiring an
unhealthy-eating partner. We further identify foods whose purchase frequency is
impacted significantly by the eating partner's healthiness of food choice.
Beyond the main results, the work demonstrates the utility of passively sensed
food purchase logs for deriving insights, with the potential of informing the
design of public health interventions and food offerings.

Social and Information Networks;Artificial Intelligence