  Online self-disclosure is perhaps one of the last decade's most studied
communication processes, thanks to the introduction of Online Social Networks
(OSNs) like Facebook. Self-disclosure research has contributed significantly to
the design of preventative nudges seeking to support and guide users when
revealing private information in OSNs. Still, assessing the effectiveness of
these solutions is often challenging since changing or modifying the choice
architecture of OSN platforms is practically unfeasible. In turn, the
effectiveness of numerous nudging designs is supported primarily by
self-reported data instead of actual behavioral information. This work presents
ENAGRAM, an app for evaluating preventative nudges, and reports the first
results of an empirical study conducted with it. Such a study aims to showcase
how the app (and the data collected with it) can be leveraged to assess the
effectiveness of a particular nudging approach. We used ENAGRAM as a vehicle to
test a risk-based strategy for nudging the self-disclosure decisions of
Instagram users. For this, we created two variations of the same nudge and
tested it in a between-subjects experimental setting. Study participants (N=22)
were recruited via Prolific and asked to use the app regularly for 7 days. An
online survey was distributed at the end of the experiment to measure some
privacy-related constructs. From the data collected with ENAGRAM, we observed
lower (though non-significant) self-disclosure levels when applying risk-based
interventions. The constructs measured with the survey were not significant
either, except for participants' External Information Privacy Concerns. Our
results suggest that (i) ENAGRAM is a suitable alternative for conducting
longitudinal experiments in a privacy-friendly way, and (ii) it provides a
flexible framework for the evaluation of a broad spectrum of nudging solutions.
