  Social media have become a significant venue for information sharing of live
updates. Users of social media are producing and sharing large amount of
personal data as a part of the live updates. A significant percentage of this
data contains location information that can be used by other people for many
purposes. Some of the social media users deliberately share their own location
information with other social network users. However, a large number of social
media users blindly or implicitly share their location without noticing it or
its possible consequences. Implicit location sharing is investigated in the
current paper. We perform a large scale study on implicit location sharing for
one of the most popular social media platform, namely Twitter. After a careful
study, we built a dataset of Turkish tweets and manually tagged them. Using
machine learning techniques we built classifiers that are able to classify
whether a given tweet contains implicit location sharing or not. The
classifiers are shown to be very accurate and efficient. Moreover, the best
classifier is employed as a browser add-on tool which warns the user whenever
an implicit location sharing is predicted from to be released tweet. The paper
provides the methodology and the technical analysis as well. Furthermore, it
discusses how these techniques can be extended to different social network
services and also to different languages.
