Spatial Analysis of News Sources

Andrew Mehler, Steven Skiena, Yunfan Bao, Xin Li, Yue Wang

Stony Brook Universitywww.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 AcquisitionSpiders - 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.

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

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

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

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 NY TimesPhiladelphia

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 SurfaceTriangle (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 AnalysisOur 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: 7.06e-09 W-Var: 7.11e-05

Var: 6.24e-07 W-Var: 3.00e-03

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 Size400 biased128 unbiased200 uniform200 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.