Abstract
In the last decade, the Internet has seen the rise of social networking as the number one online activity worldwide. To estimate the geographical location of users of social networks at a particular moment, we propose an approach to geo-tag Twitter users based only on their content of their posts. These data can later be used for local sentimental analysis, emergency detection, nding a missing person, and other novel location-based purposes. Our approach carried out a semantic analysis of tweets content to infer where in the globe a particular user is located at a given time. Based on our experimental results conducted through Amazon Mechanical Turk, the proposed framework was evaluated by 93 evaluators who assessed 654 twitter user pro les and 2,165 tweets from 17 countries. Our system could infer some geographical information for 81% of evaluated pro les. Results show 79% accuracy in identifying the user's country and 66% accuracy in identifying the users current location. This high accuracy shows our proposed methods are feasible and convincing.