Online User Location Inference Exploiting Spatiotemporal Correlations in Social Streams

Online User Loca.on Inference Exploi'ng Spa'otemporal

Correla'ons in Social Streams

Yuto Yamaguchi†, Toshiyuki Amagasa†, Hiroyuki Kitagawa†, and Yohei Ikawa‡

† University of Tsukuba ‡ IBM Research -‐ Tokyo

14/11/05 CIKM 2014 -‐ Yuto Yamaguchi 1

Tweets that help us


Shaked !!!

We can infer your home loca'on immediately

Thunder

Social and Loca'on


•  Lots of social media users

•  Frequent updates

•  User home loca'ons

Loca'on-‐based Applica'ons


•  Event Detec'on

•  Loca'on-‐based Marke'ng

•  Epidemics Analysis

Lack of home loca'ons


Most users do not disclose their home loca'ons

•  74% of TwiXer users [Cheng+, 10]

•  94% of Facebooks users [Backstrom+, 10]

Our Objec've


To infer home loca'ons of social media users

Focus & Contribu'ons


〜〜〜〜〜〜〜〜

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Time

〜〜〜〜〜〜〜〜

Our Focus

Social contents are not sta-c, but like a stream

Our Contribu.ons

1.  Online & Incremental Inference

2.  Exploi'ng Spa'otemporal features

ONLINE & INCREMENTAL INFERENCE

Contribu'on 1


Exis'ng methods: Batch inference


Exis'ng Methods

Inference Results Batch Input

Perform batch inference just once acer “enough data” is stored

è  Can’t update the results L è  What is “enough”? L è  When will it be enough? L

Our method: Online & incremental inference method


Online & incremental method

Inference Results Social Stream

Perform loca'on inference every 'me new post arrives

è Can keep the results up to date J

EXPLOITING SPATIOTEMPORAL FEATURES

Contribu'on 2


Local words


地震だ！ Steelers!

Home loca.on known

Local words: strongly correlated to a specific loca.on

Steelers!

Home loca.on unknown

Infer PiXsburgh?

Exis'ng methods: Only sta'c features


地震だ！ Thunder!

Home loca'on known

Thunder!

Home loca'on unknown

“Thunderbolt” is not a local word sta'cally

Thunder!

Thunder! Thunder!

Home loca'on known

Home loca'on known

Home loca'on known

è Can’t u.lize this word L

Can’t infer

Our method: Spa'otemporal correla'on


地震だ！ Thunder!

Home loca'on known

Thunder!

Home loca'on unknown

“Thunderbolt” can be a local word temporally è Our method can u.lize this word J

In a specific .me period

Can infer

PROPOSED METHOD OLIM: Online Loca'on Inference Method


The Algorithm


1.  divideMap() 2.  calcPopula'onDistribu'on() 3.  for post p from SocialStream 4.  user u <-‐ getUser(p) 5.  if u is loca'on-‐known 6.  updateLocalWords(p) 7.  else 8.  updateUserLoca'on(u,p) .

Preprocessing

Main

Slide 17

Slide 20

Slide 24

divideMap


Each region is treated as a categorical loca'on

Quadtree decomposi.on

L = l1, l2,…, lK{ }

Loca.on inference is reduced to a classifica.on problem

Popula'on distribu'on


…

l1 l2 l3 l4 l5 lK

What frac.on of loca.on-‐known users live in each loca.on Used for local words extrac.on

Categorical distribu'on

The Algorithm



Preprocessing

Main

Slide 17

Slide 20

Slide 24

updateLocalWords: Sliding window and word distribu'on


Sliding window with length N

e.g.) N = 5

…

l1 l2 l3 l4 l5 lK…

Word distribu'on

Where the word posted from?

updateLocalWords: Local Word Intui'on


…

…

l1 l2 l3 l4 l5 lK…

…

l1 l2 l3 l4 l5 lK…

Popula'on distribu'on Word distribu'on

Word distribu'on

KL Divergence small

Local word

…

l1 l2 l3 l4 l5 lK

Detail

updateLocalWords: Online upda'ng


Detail

Window length N is fixed

We can update KL in O(1) every .me new post arrives J

The Algorithm



Preprocessing

Main

Slide 17

Slide 20

Slide 24

updateUserLoca'on: user distribu'on


…

l1 l2 l3 l4 l5 lK

Denotes how likely this user lives in each loca'on

u

User distribu'on of u

updateUserLoca'on: update


…

l1 l2 l3 l4 l5 lK

prior

…

l1 l2 l3 l4 l5 lK

posterior

update

If user u posts local word w:

…

l1 l2 l3 l4 l5 lK…

w

Word distribu'on of w

Dirichlet-‐Mul.nomial Compound for Bayesian updates

Detail

EXPERIMENTS Accuracy & Costs


Data from TwiXer

•  Data size – 200K loca'on-‐known users in Japan

•  Geocode loca'on profiles into coordinates – 200 tweets for each user (40M in total) – 34M follow edges (for exis'ng methods)

•  90% for training; 5% for valida'on; 5% for test


Inference accuracy


Exis.ng methods

Cost per update


Be]er

Variants of ours

Exis.ng methods

Feed 40M tweets in the dataset chronologically

Conclusion

•  Proposed loca'on inference method – online & incremental inference

•  Constant 'me complexity

– exploi'ng spa'otemporal correla'on •  BeXer accuracy


Technology

Online User Location Inference Exploiting Spatiotemporal Correlations in Social Streams