Abstract
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 share 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 users. However, a large number of users blindly or implicitly share their own location without noticing it and its possible consequences. Implicit location sharing is investigated in the current paper.
We perform a large scale study on implicit location sharing detection for one of the most popular social media platform, namely Twitter. After a careful study, we prepared a training data set of Turkish tweets and manually labelled them. Using machine learning techniques we induced 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 as well. Moreover, the best classifier is employed in a browser add-on tool which warns the user whenever an implicit location sharing is predicted from just to be released tweet. The paper provides the followed 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.
This work has been supported by TUBITAK under grant number 114E132.
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Yavuz, D.D., Abul, O. (2016). Implicit Location Sharing Detection in Social Media Turkish Text Messaging. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_29
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DOI: https://doi.org/10.1007/978-3-319-51469-7_29
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