User Location Inference
using Microblog Messages
IBM Research – TokyoYohei Ikawa
Miki Enoki
Michiaki Tatsubori

StorylMotivation: Missing user location information inmicroblog messages (such as Twitter tweets)llProposal: Tweet location inference from message
content and text collocationllRealtime, situational messages from microblogs can help
with disaster management, especially if the messages areassociated with locations

Associates general words such as “office” or “home” withactual places mentioned shortly before or after

Evaluation: Distance errors of inferred user locations
lThe proposed method performs relatively well, especiallywhen the permitted distance error range is small

Microblogs Associated with Locations
Can Help Disaster Management

An accident has
happened!
I was stuck intraffic…
ll

Associating each message
with physical location helps
understand what incidents areoccurring
Useful for municipal
emergency response centers

I got caught in a
traffic jam!

Traffic jam due to an
accident is spreading!

Problem: Geotags in Microblogs
are Rarely Available
l <1% of messages are geo-tagged on Twitterl Possible Reasons:

Explicit activation of the geotag functionl Privacy concernsl

Our Goal: Estimate Each Tweet
Location using Message Contentl

Outline of our approachlll

Find clues to user’s current locations
Associate keywords in other messages created just before orjust after a message with that location.
For a new message, estimate the location associated with thekeywords if the new message contains some of the keywords

08:32
08:40

12:05
12:17

：
I’m in Lyon
Arrived at office：：
Eating lunch at office
Stuck in traffic..
：office à Lyon

Current location is Lyon?
Current location is Lyon?

Proposed Method: AssociatingGeneral Words with LocationslTraining steplll

Estimation stepll

08:25
08:32
08:40

12:05

Find clues to users’ current locations
Associate keywords in messages created close to the time t(within 10 minutes) with the location.
For a new message, estimate the location associated with thekeywords if the new message contains some of the keywords
We use cosine similarity to calculate the keyword set similarity：
Eating breakfast at cafe
I’m at Lyon
Arrived at office：：
Eating lunch at office：Associate location
with keyword set

Keyword set

Locationbreakfast, cafe

LyonofficeLyon

What is the location associated with “office”?

Current location is Lyon?
Estimate locationfrom keyword set

Clues to User’s Current LocationlGeotagsllMessages from location services
lllGeotags explicitly represent the users’ current locations
A service for broadly sharing the present location
A user’s current location can be tracked since messages from eachlocation service have a specific format

Place names
lll

Location Services

Message Formats

FoursquareI’m at [Location] ([Address])
Recognize place names in each[Comment] (@ [Location])message with text analysis
Loctouch
Touched at [Location]
Place names in a message are(in Japanese)[Comment] @ Touched at [Location]
sometimes not the current location
The accuracy is lower than the Geotag and Location-service approaches

We used messages from location services
to train for our prototype this timeExperiment: Evaluate ourApproach using Twitter Datall

Outlinel Evaluate the effectiveness of the proposed method for Twitterusers
Evaluation methodl Validate locations for messages with geotags
l Precisely evaluate the distance errors between the estimated andactual locations by calculating the distances between the pairs ofpointsl Compare three methods:l Content-based method (training for each user)l Content-based method (training for all of the users)l Naïve method (training for each user)§The estimated result is always the place from which the user has
issued the largest number of messages in the pastResult: relationship between
distance errors and precisionlPrecision: accuracy of the location estimates when distance error iswithin 0.5 km, 1.0 km, 3.0 km, 5.0 km, 10 km, and 30 km
Improved precisions
with the proposed method

0.6
0.5

Content-based
foreach user

Precision

0.4(training for each user)

0.3

User-profile based

baseline(training for each user)

0.2
0.1
00

5

10

15

20

25

30Error (kilometer)lContent-based method with training for each user is efficient
especially when the permitted distance error range is small

Prepared Training Data andQuery Datall

Data collection from Twitterl Select 110 Twitter users who use location services
l Collect 3,200 messages sent by these users
Preparing training data and query datal Training data: 12,463 messages
lllQuery data: 20,535 messagesllGeo-tagged messages from location services
Messages associated with locations
Training datain the training step
08:25
Eating breakfast at cafe
Geo-tagged messages other thantraining data

08:32
08:40

I’m at Lyon
Arrived at office

Query data
12:05
Eating lunch at office

Estimate locations for query data,and calculate precise distance errors

geotaggeotagResult: relationship between
distance errors and recalll

Recall: Accuracy of the location estimation for all of the query data
within the range of distance error within 0.5 km, 1.0 km, 3.0 km, 5.0km, 10 km, and 30 km
Distance error
Training approach

l0.5km

1.0km

3.0km

5.0km

10km

30km

for each user

0.07

0.09

0.12

0.14

0.17

0.22

with all of the users

0.06

0.07

0.10

0.13

0.16

0.25

Numbers of estimates for 20,535 query messages
For each user: 7,658 messagesl With all of the users: 16,380 messages
à Recall for all users is slightly lower than for the each-user cases because
the all-user case fails to estimate any location for many of the messagesll

An approach that trains for each user is efficientl

An approach to training with all of the users causes failures forexpressions associated with the user’s specific locations

Results: Distribution of estimateswith training for each user
800

incorrect
correct

Number of query data

700
600
500
400
300
200
10001100
UserllThe failures for some users substantially reduce their accuracy
Possible to improve the accuracy to deal with these cases

Conclusions and Future Workl

Conclusions
lll

We proposed a tweet location inference method, whichassociates general words with locations and estimates thelocation for each message
We evaluated several location estimation approaches, andconfirmed that the proposed method of training for each user
has advantages, especially when the distance error is small

Future Worklll

Use of more approaches to location clues detection
Better models incorporating temporal information
Experiments with larger and/or various datasetsThank you!