Implicit Location Sharing Detection in Social Media from Short Turkish Text

TL;DR

This paper investigates implicit location sharing in Turkish tweets, developing machine learning classifiers that accurately detect such sharing and providing a browser tool to warn users, thereby enhancing privacy awareness.

Contribution

It introduces a novel dataset of Turkish tweets, develops effective classifiers for implicit location sharing detection, and presents a practical browser add-on for user privacy protection.

Findings

Classifiers achieve high accuracy in detecting implicit location sharing.

The browser tool effectively warns users about potential privacy risks.

Methodology can be extended to other languages and social media platforms.

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 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.