  The increased popularity and ubiquitous availability of online social
networks and globalised Internet access have affected the way in which people
share content. The information that users willingly disclose on these platforms
can be used for various purposes, from building consumer models for
advertising, to inferring personal, potentially invasive, information. In this
work, we use Twitter, Instagram and Foursquare data to convey the idea that the
content shared by users, especially when aggregated across platforms, can
potentially disclose more information than was originally intended. We perform
two case studies: First, we perform user de-anonymization by mimicking the
scenario of finding the identity of a user making anonymous posts within a
group of users. Empirical evaluation on a sample of real-world social network
profiles suggests that cross-platform aggregation introduces significant
performance gains in user identification. In the second task, we show that it
is possible to infer physical location visits of a user on the basis of shared
Twitter and Instagram content. We present an informativeness scoring function
which estimates the relevance and novelty of a shared piece of information with
respect to an inference task. This measure is validated using an active
learning framework which chooses the most informative content at each given
point in time. Based on a large-scale data sample, we show that by doing this,
we can attain an improved inference performance. In some cases this performance
exceeds even the use of the user's full timeline.
