Using Large Scale Aggregated Knowledge for Social Media Location Discovery

Geospatial analysis of location-enabled social media networks can be utilized to generate vital insights in areas where situational awareness is important, such as disaster prevention and crisis response. However, several recent approaches struggle under the challenge that only a small fraction of the data is actually provided with precise geo-tags or even GPS information of their origin. In this work we introduce two strategies that are suitable to assign probable locations of origin to social media messages of unknown locations. They are based on aggregated knowledge about the author and/or the textual content of the message. Using our prototype implementation and a collected dataset comprising more than one year of geolocated Twitter data, we evaluate the effectiveness of our strategies. Our results show that we can locate up to 74% of all messages that were written in specific cities and about 20% of messages written in specific districts.