Zubiaga, Arkaitz, Voss, Alex, Procter, Rob, Liakata, Maria, Wang, Bo and Tsakalidis, Adam (2017) Towards real-time, country-level location classification of worldwide tweets. IEEE Transactions on Knowledge and Data Engineering, 29 (9). pp. 2053-2066. doi:10.1109/TKDE.2017.2698463 ISSN 1041-4347.

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Abstract

The increase of interest in using social media as a source for research has motivated tackling the challenge of automatically geolocating tweets, given the lack of explicit location information in the majority of tweets. In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets 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 tweets can still be leveraged for classification of new tweets. 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 tweets. 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 tweets to classify new tweets, 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.

Item Type:	Journal Article
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) H Social Sciences > HM Sociology Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Microblogs -- Classification, Twitter (Firm) , Geographical positions
Journal or Publication Title:	IEEE Transactions on Knowledge and Data Engineering
Publisher:	IEEE Computer Society
ISSN:	1041-4347
Official Date:	1 September 2017
Dates:	Date Event 1 September 2017 Published 27 April 2017 Available 24 April 2017 Accepted
Volume:	29
Number:	9
Page Range:	pp. 2053-2066
DOI:	10.1109/TKDE.2017.2698463
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	3 May 2017
Date of first compliant Open Access:	8 May 2017
Funder:	Seventh Framework Programme (European Commission) (FP7), Warwick Impact Fund, Economic and Social Research Council (Great Britain) (ESRC), Engineering and Physical Sciences Research Council (EPSRC)
Grant number:	Grant No. 611233 (FP7), EP/K503940/1, EP/L016400/1, EP/K000128/1 (EPSRC)
Related URLs:	Related dataset

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