Spatial Analysis of News Sources
Andrew Mehler, Steven Skiena,
Yunfan Bao, Xin Li, Yue Wang
Stony Brook University
www.textmap.com
Computational News Analysis
• Lydia: Large scale newspaper analysis.
• Obtain data on how the volume of news
coverage varies by location.
• Our paper describes how we calculate,
display, and evaluate spatial bias in news
sources.
Who Is Running For President?
Stony Brook University
Mark Foley Scandal
Who is Looking for a Manager?
Steve Nash’s Teams
Lydia (textmap.com)
Data-maps are a component of the Lydia system. The data
generated from the Lydia system drives the data-map creation.
Monitors ~1000 newspapers every day and also other sources.
Components of Lydia include….
Named Entity Recognition
Saddam Hussein’s chief lawyer warned Sunday of worsening
violence in Iraq and chaos across the Mideast if the ex-president
is sentenced to death at his trial for a crackdown on a Shiite
Muslim village in the 1980s. Khalil al-Dulaimi also said he would
break a month long boycott and attend proceedings Monday when
Saddam's second trial resumes on separate charges of genocide
against the Kurds.
Segmentation and Classification
Saddam Hussein’s chief lawyer warned Sunday of worsening
violence in Iraq and chaos across the Mideast if the ex-president
is sentenced to death at his trial for a crackdown on a Shiite
Muslim village in the 1980s. Khalil al-Dulaimi also said he would
break a month long boycott and attend proceedings Monday when
Saddam's second trial resumes on separate charges of genocide
against the Kurds.
Favorite Things
Social Network
Juxtaposition Analysis
Article Categorization
Related Work
• Visualizing Data (Tufte)
• Geographic Visualization (Slocum, McMaster, Kessler, Howard)
• Data Maps / Color Schemes (Brewer)
• Quantitative Geography (Fotheringham, Brunsdon, Charlton)
• Spatial Data-Mining (Miller, Han)
• Spatial Interpolation / Smoothing (Fuentes, Stein)
Outline of this Talk
News/Data Acquisition
Source-Influence Modeling
Spatial Visualization
Identification of Spatially Biased Maps
Conclusions
News Acquisition
Spiders - Programs that crawl a web domain and download all of
the pages. Universal Spider built using wget.
Still need customization
• Cookies / Logins
• Page Structure / formatting / Advertisements
• Each paper ~ 40-130MB in 20-80 minutes.
• ~800 U.S. papers and ~300 foreign papers.
Duplicate Articles?
• Syndication, Persistence, Ongoing Stories
Duplicate Detection
Despite playing without three injured defensive starters and losing
another early, the Giants held Tampa Bay to 174 total yards and set up a
score with a turnover deep in Buccaneers' territory in a 17-3 victory
Sunday that gave New York its fourth straight win.
Despite playing without three injured defensive starters and losing
another early, the Giants held Tampa Bay to 174 total yards and set up a
score with a turnover deep in Buccaneers' territory in a 17-3 victory
Sunday.
Character Windows
Despite playing without three injured defensive starters and losing
another early, the Giants held Tampa Bay to 174 total yards and set up a
score with a turnover deep in Buccaneers' territory in a 17-3 victory
Sunday that gave New York its fourth straight win.
Despite playing without three injured defensive starters and losing
another early, the Giants held Tampa Bay to 174 total yards and set up a
score with a turnover deep in Buccaneers' territory in a 17-3 victory
Sunday.
Most Windows Equal in Duplicates
Despite playing without three injured defensive starters and losing
another early, the Giants held Tampa Bay to 174 total yards and set up a
score with a turnover deep in Buccaneers' territory in a 17-3 victory
Sunday that gave New York its fourth straight win.
Despite playing without three injured defensive starters and losing
another early, the Giants held Tampa Bay to 174 total yards and set up a
score with a turnover deep in Buccaneers' territory in a 17-3 victory
Sunday.
Hash Codes For Windows
Document 1: 17, 29, 113, 30, 25, 10, 130, 128, 50, 119, 190, 1979
Document 2: 17, 29, 113, 30, 25, 10, 130, 128, 50
Size Reduction
Document 1: 17, 29, 113, 30, 25, 10, 130, 128, 50, 119, 190, 1979
Document 2: 17, 29, 113, 30, 25, 10, 130, 128, 50
Size Reduction
Document 1: 17, 29, 113, 30, 25, 10, 130, 128, 50, 119, 190, 1979
Document 2: 17, 29, 113, 30, 25, 10, 130, 128, 50
Outline of this Talk
News/Data Acquisition
Source-Influence Modeling
Spatial Visualization
Identification of Spatially Biased Maps
Conclusions
Combining News Influence
How do we combine all the newspapers that are read in
an area?
In Bloomsburg, PA people might read
• The New York Times
• The Philadelphia Inquirer
• The Bloomsburg Press Enterprise
What Is Reflective of Bloomsburg’s Interests?
Linear Decay Model
Bloomsburg
Philadelphia
NY Times
Influence Model
To estimate the contributions of different sources, we develop
an influence model.
The influence is a function on cities and sources, quantifying
how influential a source is in a particular city.
Influence(New York Times, Baltimore) = ?
The frequency of reference estimate for a city is then a
weighted average over the sources.
F(Knicks, NY) =
∑F(Knicks,s)*influence(s,NY) / ∑influence(s,NY)
Readership Estimate
The readership of a paper is estimated by combining the
papers circulation with its alexa.com rpm (reach per million).
We can then estimate the radius of a newspapers influence by
making 10% of the population covered equal the readership.
The influence function decays linearly with distance from the
source, and 0 outside its radius of influence.
• Big papers have a larger influence than small papers.
• Potential readership base not a factor.
• Is linear decay the right model?
• Some large papers have national distributions.
Outline of this Talk
News/Data Acquisition
Source-Influence Modeling
Spatial Visualization
Identification of Spatially Biased Maps
Conclusions
Visualization Issues
• Representing United States Surface
Triangle (Shewchuk) used to create a Delauney
triangulation of the cities.
• Interpolating
Surface from Point
Data (cities)
Visualization
Mesa/openGl used to render maps.
Relative color scale, max heat hottest red.
Absolute Color Scale
2 maps directly comparable
Outline of this Talk
News/Data Acquisition
Source-Influence Modeling
Spatial Visualization
Identification of Spatially Biased Maps
Conclusions
Which Maps are Interesting?
How can we Identify the Terms With A Geographic Bias?
Don’t want to look through all 200,000 entities!
How do we Quantify Geographic Bias?
Variance Analysis
Our Analysis Gives frequency estimates for 25,374 cities.
We defined 2 measures based on variance.
• Variance: The variance of the 25,374 values.
• Weighted Variance: The variance divided by the mean.
Var: 6.24e-07
W-Var: 3.00e-03
Var: 7.06e-09
W-Var: 7.11e-05
Can’t distinguish a bipolar map from a checkerboard map.
Component Analysis
Consider what happens to the number of connected components
if you only consider cities above a certain value.
Component Analysis
Consider what happens to the number of connected components
if you only consider cities above a certain value.
Component Analysis
Consider what happens to the number of connected components
if you only consider cities above a certain value.
Component Analysis
In a biased map, we expect the largest values to be clustered
together.
Component Analysis
In an unbiased map, we expect many random clusters of high
heat. Not the single cluster we expect in biased maps.
Component Measures
• Largest Gap: The value of the largest gap. A large gap
suggests the entity is drawn from 2 different distributions,
local and national.
• Weighted Gap: Largest divided by max.
• Percentage Gap: Percentage Change.
Evaluating Bias Measures
To evaluate the measures, we made 4 sets of data maps…
Random Entity: Uniform
Random Entity: Binomial
Unbiased Entity
Biased Entity
Results
Data Set Size
400 biased
128 unbiased
200 uniform
200 binomial
Discriminating Real Data
Future Work
• Improved Map visualization
• Sentiment Data Maps.
• Animated maps showing temporal changes in popularity.
• Improved influence models.
• Empirical justifications of models.
• Improved bias estimators.