TOWARDS REAL TIME,COUNTRY LEVEL LOCATION CLASSIFICATION OF WORLDWIDE- FACEBOOK

Abstract

The increase of interest in using social media as a source for research has motivated tackling the challenge of
automatically geolocating messages , given the lack of explicit location information in the majority of messages. In
contrast to much previous work that has focused on location classification of messages restricted to a specific country,
here we undertake the task in a broader context by classifying global messages at the country level, which is so far
unexplored in a real-time scenario. We analyse the extent to which a tweet’s country of origin can be determined by
making use of eight tweet-inherent features for classification. Furthermore, we use two datasets, collected a year apart
from each other, to analyse the extent to which a model trained from historical messages can still be leveraged for
classification of new messages. With classification experiments on all 217 countries in our datasets, as well as on the top
25 countries, we offer some insights into the best use of tweet-inherent features for an accurate country-level
classification of messages. We find that the use of a single feature, such as the use of tweet content alone – the most
widely used feature in previous work – leaves much to be desired. Choosing an appropriate combination of both tweet
content and metadata can actually lead to substantial improvements of between 20% and 50%. We observe that tweet
content, the user’s self-reported location and the user’s real name, all of which are inherent in a tweet and available in a
real-time scenario, are particularly useful to determine the country of origin. We also experiment on the applicability of
a model trained on historical messages to classify new messages, finding that the choice of a particular combination of
features whose utility does not fade over time can actually lead to comparable performance, avoiding the need to retrain.
However, the difficulty of achieving accurate classification increases slightly for countries with multiple commonalities,
especially for English and Spanish speaking countries.