inferring(author(locaon(in(social(media sanjay(krishnan...
TRANSCRIPT
Inferring Author Loca1on in Social Media
Sanjay Krishnan and Evan Sparks CS 294-‐1 Behavioral Data Mining
Mo1va1on • Users in Social Media systems (eg. TwiLer) oNen self-‐report loca1on. – Roughly 1.5% of tweets.
• Loca1on aLributes may be incomplete, masked by privacy seTngs, or missing altogether.
Problem Statement and Dataset
• 1-‐year of the TwiLer firehose (10% sample). • Given a tweet, predict the current U.S state loca1on of the author
Related Work
• Geo-‐loca1on inference is a well studied in en1ty resolu1on and image recogni1on
• Content based inference on blogs [Fink et al. 2008]
• Intra-‐city loca1ons based on TwiLer content [Cheng et al. 2010]
• Infer loca1ons from TwiLer follower networks [Davis et al. 2011]
• Applied to other micro-‐blogs [Ikawa 2012]
Models and Algorithms
• Geometry of the problem is complex – Mul1-‐class classifica1on problem.
• Bag of NER Features • Predic1on: State (from lat, long) – Efficient point-‐in-‐polygon lookup.
• Mul1nomial Logis1c Regression – Implemented on Spark.
• Stochas1c Gradient Descent
Analysis Pipeline
• 2.6 TB, gzip 1 Year of 10% Firehose
• 42 GB, gzip Tweets with Loca4on
• 6GB, raw NER Tagged Features Top 60k Features
Model mariangeeve In/O the/O Apple/O store/O in/O Atlanta/LOCATION ./O @Reinier_Sanders/O with/O Janneke/PERSON trying/O to/O buy/O an/O IPad/O ./O hMp://t.co/AtX5E9Vc/O ga
Results
• F1 score by state. • Typically between 0.4 and 0.6.
• Precision vs. Recall. • Tends to be a liLle lower in low popula1on states. – R=0.45, p=0.003
25
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−120 −100 −80Longitude
Latit
ude
0.3
0.4
0.5
0.6F1
F1 by State
Feature Engineering Helps
• Named en1ty recogni1on (NER) loca1on features account for most of the predic1ve power.
0.0
0.2
0.4
0.6
0.0
0.2
0.4
0.6
F1F1.No.Locations
ALAR AZ CACO CTDE FLG
A IA ID IL IN KS KY LA MA
MD
ME MI
MN
MO
MS
MT
NC ND NENH NJNM NV NYO
HO
KO
R PA RISC SD TN TX UT VA VTW
A WI
WV
WY
State
F1.S
core
F1 Before and After Feature Extraction
Precision and Recall
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AL
AR
AZ
CA
CO
CT
DE
FL
GAIA
IDIL
INKSKY LA
MA
MD MEMIMN MO
MS
MTNC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR PA
RISC
SD
TN
TX
UTVAVT
WAWI
WV
WY
0.25
0.50
0.75
0.2 0.4 0.6 0.8Precision
Reca
ll
Precision vs. Recall by State
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ALARAZCACOCTDEFL
GAIAIDILIN
KSKYLAMAMDMEMI
MNMOMSMTNCNDNENHNJ
NMNVNYOHOKORPARI
SCSDTNTXUTVAVT
WAWI
WVWY
AL ARAZCACOCTDE FL GA IA ID IL IN KSKY LAMAMDME MIMNMOMSMTNCNDNENHNJNMNVNYOHOKORPA RI SCSDTN TX UT VA VTWAWIWVWYPrediction
Actu
al
1.000000
7.389056
54.598150
403.428793
Count
Classification − Predictions vs. Actuals
• Populated states tend to have lots of false posi1ves, small western states tend to be more precise.
Misclassifica1ons and Geography
• False posi1ves have a strong regional dependence.
25
30
35
40
45
50
−120 −100 −80Longitude
Latit
ude Misclassification
CaliforniaTexas
Misclassification Toward California or Texas
Neat Features – Apple/Organiza1on
• Good predictors are not always loca1ons.
• Apple tagged as an organiza(on. – States where people have money?
– States where there’s an Apple store?
– Connec1on?
25
30
35
40
45
50
−120 −100 −80Longitude
Latit
ude
0.0
0.1
0.2
0.3
Apple
Strength of Apple (Organization) Feature by State
Summary
• Designed a distributed framework for inferring author loca1on from NER tagged textual data.
• Implemented Mul1nomial Logis1c Regression in Spark.
• PreLy good: some classes had F1 scores > 0.6 • Regional and social insights from the model. – Texas vs. California – Apple