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Social media have become a significant venue for information sharing of liveupdates. Users of social media are producing and sharing large amount ofpersonal data as a part of the live updates. A significant percentage of thisdata contains location information that can be used by other people for manypurposes. Some of the social media users deliberately share their own locationinformation with other social network users. However, a large number of socialmedia users blindly or implicitly share their location without noticing it orits possible consequences. Implicit location sharing is investigated in thecurrent paper. We perform a large scale study on implicit location sharing forone of the most popular social media platform, namely Twitter. After a carefulstudy, we built a dataset of Turkish tweets and manually tagged them. Usingmachine learning techniques we built classifiers that are able to classifywhether a given tweet contains implicit location sharing or not. Theclassifiers are shown to be very accurate and efficient. Moreover, the bestclassifier is employed as a browser add-on tool which warns the user wheneveran implicit location sharing is predicted from to be released tweet. The paperprovides the methodology and the technical analysis as well. Furthermore, itdiscusses how these techniques can be extended to different social networkservices and also to different languages.

Implicit Location Sharing Detection in Social Media from Short Turkish Text