ACL2015 Poster: Twitter User Geolocation Using a Unified Text and Network Prediction Model

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This document summarizes a research paper on predicting the geographic location of Twitter users using a unified text and network prediction model. The model uses both text-based features from users' tweets as well as the network of mentions between users. It achieves state-of-the-art results on three Twitter geolocation datasets by combining text and network-based predictions through a modified label propagation technique. The unified model outperforms both individual text-based and network-based models for predicting locations.

Data & Analytics

Twitter User Geolocation Using a Uniﬁed Text and
Network Prediction Model
Afshin Rahimi, Trevor Cohn and Timothy Baldwin
Department of Computing and Information Systems, The University of Melbourne
OVERVIEW
Task: Where does @ShvwnK live?
Input: user, concatenated tweet text, mention-list
Output: latitude/longitude
(known for training users, predicted for test users)
Datasets: 3 Twitter geolocation datasets (#users in parenthesis)
GeoText (9.5K), Twitter-US (450K) and Twitter-World (1.4M).
TEXT-BASED MODEL
Logistic regression with l1 regularisation
over k-d tree discretisation of latitude/longitude.
top features of NYC use of “upstate” in U.S.
NETWORK-BASED MODEL
Label propagation in a collapsed network:
• Build the graph using @-mentions.
• Use training nodes as seed (labelled samples).
• Infer the test labels by Modiﬁed Adsorption (Talukar
and Crammer, 2009).
argmin
ˆY
c( ˆY ) =
l
µ1
Match seed
(Yl − ˆYl)T
S(Yl − ˆYl) + µ2
ˆY T
l L ˆYl
Smooth labels
0.7 0.5
0.01
new label
estimate
FROM @-MENTION TO COLLAPSED NETWORK
@-mention Network Collapsed Network + Text Dongle Nodes
labelled
nodes
unlabelled
nodes
mentioned
nodes
text dongle nodes
celebrity
UNIFIED MODEL: NETWORK & TEXT
• For connected users, Network-based models are
more accurate.
• For disconnected users (about 20% of the nodes),
text-based models are more accurate.
• Solution: Utilise both text and network!
• For each test node, attach a text dongle node car-
rying text-based predictions.
• Add the text dongle nodes to seed nodes (like train-
ing nodes).
• Use Modiﬁed Adsorption to infer the labels.
“CELEBRITIES” DON’T GEOLOCATE
• “Celebrities” (highly mentioned users) are
connected from everywhere.
• They connect lots of people.
• Solution: Remove users with more than T mentions.
• Results in sparser graphs (tractable inference)
and more accurate geolocation.
TUNING T (TWITTER-US)
2 5 15 50 500 5k
Celebrity threshold T (# of mentions)
700
720
740
760
780
800
820
840
860
Meanerror(inkm)
Mean error
Graph size
105
106
107
108
109
Graphsize(#edges)
Decreasing T results in: sparser graph, lower mean error.
RESULTS
State of the art results over all three datasets!
GEOTEXT TwitterUS TwitterWorld
600
800
1000
1200
1400
1600
MeanError(km)
Network-based Model (This work)
Unified Model (This work)
Network-based: Rahimi et al. (NAACL2015)
Text-based: Rahimi et al. (NAACL2015)
Text-based: Wing and Baldrige (EMNLP2014)
Text-based: Cha et al. (ICWSM2015)
larger dataset
−−−−−−−−−→

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1. Twitter User Geolocation Using a Unified Text and Network Prediction Model Afshin Rahimi, Trevor Cohn and Timothy Baldwin Department of Computing and Information Systems, The University of Melbourne OVERVIEW Task: Where does @ShvwnK live? Input: user, concatenated tweet text, mention-list Output: latitude/longitude (known for training users, predicted for test users) Datasets: 3 Twitter geolocation datasets (#users in parenthesis) GeoText (9.5K), Twitter-US (450K) and Twitter-World (1.4M). TEXT-BASED MODEL Logistic regression with l1 regularisation over k-d tree discretisation of latitude/longitude. top features of NYC use of “upstate” in U.S. NETWORK-BASED MODEL Label propagation in a collapsed network: • Build the graph using @-mentions. • Use training nodes as seed (labelled samples). • Infer the test labels by Modified Adsorption (Talukar and Crammer, 2009). argmin ˆY c( ˆY ) = l µ1 Match seed (Yl − ˆYl)T S(Yl − ˆYl) + µ2 ˆY T l L ˆYl Smooth labels 0.7 0.5 0.01 new label estimate FROM @-MENTION TO COLLAPSED NETWORK @-mention Network Collapsed Network + Text Dongle Nodes labelled nodes unlabelled nodes mentioned nodes text dongle nodes celebrity UNIFIED MODEL: NETWORK & TEXT • For connected users, Network-based models are more accurate. • For disconnected users (about 20% of the nodes), text-based models are more accurate. • Solution: Utilise both text and network! • For each test node, attach a text dongle node car- rying text-based predictions. • Add the text dongle nodes to seed nodes (like training nodes). • Use Modified Adsorption to infer the labels. “CELEBRITIES” DON’T GEOLOCATE • “Celebrities” (highly mentioned users) are connected from everywhere. • They connect lots of people. • Solution: Remove users with more than T mentions. • Results in sparser graphs (tractable inference) and more accurate geolocation. TUNING T (TWITTER-US) 2 5 15 50 500 5k Celebrity threshold T (# of mentions) 700 720 740 760 780 800 820 840 860 Meanerror(inkm) Mean error Graph size 105 106 107 108 109 Graphsize(#edges) Decreasing T results in: sparser graph, lower mean error. RESULTS State of the art results over all three datasets! GEOTEXT TwitterUS TwitterWorld 600 800 1000 1200 1400 1600 MeanError(km) Network-based Model (This work) Unified Model (This work) Network-based: Rahimi et al. (NAACL2015) Text-based: Rahimi et al. (NAACL2015) Text-based: Wing and Baldrige (EMNLP2014) Text-based: Cha et al. (ICWSM2015) larger dataset −−−−−−−−−→

ACL2015 Poster: Twitter User Geolocation Using a Unified Text and Network Prediction Model

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ACL2015 Poster: Twitter User Geolocation Using a Unified Text and Network Prediction Model