Exploiting Text and Network Context for Geolocation of Social Media UsersAfshin Rahimi,♥ Duy Vu,♠ Trevor Cohn,♥ and Timothy Baldwin♥

♥ Department of Computing and Information Systems, ♠ Department of Mathematics and Statistics, The University of Melbourne

OVERVIEW

Task: Find the location of Twitter users based on text and net-work information

Previous Shortcoming: No comparison of text-based andnetwork-based models, no use of both.

Datasets: 3 Twitter geolocation datasets:GeoText, Twitter-US, Twitter-World.

Sample Format: userid, text, mention-list, latitude/longitude

YOU ARE WHERE YOUR WORDS SAY YOU ARE

Usage of mountain in U.S.

TEXT-BASED MODEL (LR)

Logistic regression with l1 regularisationover k-d tree discretisation of latitude/longitude.

130 120 110 100 90 80 70 60Longitude

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Lati

tude

YOU ARE WHERE YOUR FRIENDS ARE

Most of our online interactions are local.

Twitter mention

NETWORK-BASED MODEL (LP)

Label Propagation in @-mention Network:

• Build an @-mention network.

• Initialise the location of training nodes with theirknown location.

• Iteratively update non-training nodes’ locationto the median of their neighbours.

• Converges after 10 iterations.

NETWORK VERSUS TEXT

• For connected users, Network-based modelsare more accurate.

• For disconnected users (about 20% of thenodes), Text-based models are more accurate.

• Solution: Utilise both text and network informa-tion together!

LABEL PROPAGATION OVER TEXT PREDICTIONS

• Initialise training nodes with their known locationand test nodes with their text-based prediction.

• Iteratively update the location of non-trainingnodes to the median of their neighbours.

• Converges after 10 iterations.

• Isolated test nodes will keep their text-basedprediction.

DENVER’S TOP FEATURES

RESULTS

State of the art results over all three datasets!

GEOTEXT TwitterUS TwitterWorld0

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Text-based Method (LR)Network-based Method (LP)Hybrid Method (LP-LR)Wing and Baldrige (2014)Ahmed et al. (2013)