1. Summary Presented by : Aishwarya Deep Shukla
You are Where you TWEET: A content-BASED approach to GEO-locating twitter users
Summary Presented by : Aishwarya Deep Shukla

2. Problem Definition
Only 26% of Twitter users list their location (city), the rest do not
Just 0.42% use geo-tagging as of 2009 (timeframe of this study)
Can we accurately guess the users location from just the tweet text ?

3. Introduction
Twitter users slow to adopt geo tagging per tweet
Geo-location of user important to understand trends
Sparsity affects the geo-location applications
Can we predict a user’s location based purely on the content of the user’s tweets ?
Key Intuition: Specific keywords are more likely to be associated with a particular city/location

4. Challenges in locating the user with just tweet content
Twitter status updates are noisy !
Twitter users rely on shorthand, sms language – it is inconsistent and hard to text mine
Even if location sensitive data is isolated from a user’s tweets, it might be error prone as user might have interest in any other location too, other than his present location
User may have more than one location associated, E.g: travellers

5. Related Work
Probabilistic language models based on the flickr photo tags – Serdyukov et. al
Probablistic model looking at text + visual – Crandall et. Al
Applications:
Detecting earthquakes with real time twitter data
- Each user is a treated as a sensor
Detecting news origin location, and diffusion pattern

6. Data Collection
Crawl through twitter public timeline API - random sampling
Breadth First Search > Crawl Social Edges
DATA
29 million status updates of over 1 million users
72% of the profiles have no location information listed
7% have bad location information (Eg: Wonderland)
21% have a city name listed in their profile
The data distribution is representative of the population distribution

7. Evaluation Setup Location Estimation problem:
Stweets(u) - Set of tweets by user u
Estimate the probability of the user u being in city i: p(i| Stweets(u) ), such that lest(u) is actual location of the user
Test data:
Extract the tweets of all users who have actual location listed (Coordinates) and use it to check algorithm accuracy
Metrics
Error Distance  ErrDist(u)= d(lact(u), lest(u))
AvgErrDist(U)  COPY THE FORMULA
Accuracy (U)  COPY THE FORMULA

8. Location Determination Algorithm
Select training dataset
Associate real location with frequency of words used at that location
Run it on the test data to predict location
Accuracy: 10.12% 
Step 1 Baseline
Determine words with high spatial focus
These words are typically very specific to a place
Accuracy:49.8%
Step 2 Identifying local
Smoothing
Accuracy: 51%
Step 3 Optimization

9. ESTIMATION ALGORITHM: BASELINE PROBABLITY
Baseline Location Estimation
Training data of 130,689 users
Plot their tweets
Calculate estimated probability based on the formula
P(i|Swords(u))= ∑ (p(i\w)*p(w))
Test Result : only 10.12% of the 5119 test users are in the 100 miles of the estimated location this way

10. OPTIMIZATOIN Identify Local Words in Tweets
Words with more compact scope compared to other words
Determining Spatial Focus: Cd –α
(C)- Focus , Dispersion (α)
Determine the focus and dispersion

11. State Level Lattice based Model Based Tweet Sparsity
Large number of “tiny” word distibutions – words issued sparingly and from only a few cities
Smoothing approaches
State Level
Aggregate the probability of a word by state
Lattice based
Aggregate by 1 X 1 square degrees
Model Based
Spatial focused word model

12. Experimental Results Goals of the test experiments
Does classification on spatial distribution help ? -- YES
How much do different smoothing techniques help?
Impact of amount of information about a particular user (via tweets) (Count. of tweets)

13. Estimation Quality: Number of Tweets

14. Comments Novel approach to locate users based on their tweeted text
Users with geo information has steadily increased to 45% now , more than double of when this paper was authored.
Algorithm to locate users by tweet this way, can also work for other social media networks and even blogs.
Isn't there a self-selection bias in who chooses to share location … Is it okay to assume overall algorithm accuracy based on the tests results ?
Suggestions
Why not use hashtags too and develop a geo-locator based on hashtags ?