Spatial Analysis of News Sources

1. Spatial Analysis of News Sources Stony Brook University www.textmap.com Andrew Mehler, Steven Skiena, Yunfan Bao, Xin Li, Yue Wang

2. 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. Computational News Analysis

3. Who Is Running For President? Stony Brook University

4. Mark Foley Scandal

5. Who is Looking for a Manager?

6. Steve Nash’s Teams

7. 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…. Lydia (textmap.com)

8. 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.

9. 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.

10. Favorite Things

11. Social Network

12. Juxtaposition Analysis

13. Article Categorization

14. 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) Related Work

15. News/Data Acquisition Source-Influence Modeling Spatial Visualization Identification of Spatially Biased Maps Conclusions Outline of this Talk

16. 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 News Acquisition

17. 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. Duplicate Detection

18. 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

19. 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

20. 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

21. 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

22. 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

23. News/Data Acquisition Source-Influence Modeling Spatial Visualization Identification of Spatially Biased Maps Conclusions Outline of this Talk

24. 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? Combining News Influence

25. Linear Decay Model NY Times Bloomsburg Philadelphia

26. 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) Influence Model

27. 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. Readership Estimate

28. News/Data Acquisition Source-Influence Modeling Spatial Visualization Identification of Spatially Biased Maps Conclusions Outline of this Talk

29. Representing United States Surface Triangle (Shewchuk) used to create a Delauney triangulation of the cities. • Interpolating Surface from Point Data (cities) Visualization Issues

30. Mesa/openGl used to render maps. Relative color scale, max heat hottest red. Visualization

31. 2 maps directly comparable Absolute Color Scale

32. News/Data Acquisition Source-Influence Modeling Spatial Visualization Identification of Spatially Biased Maps Conclusions Outline of this Talk

33. How can we Identify the Terms With A Geographic Bias? Don’t want to look through all 200,000 entities! Which Maps are Interesting? How do we Quantify Geographic Bias?

34. 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. Variance Analysis 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.

35. Consider what happens to the number of connected components if you only consider cities above a certain value. Component Analysis

36. Consider what happens to the number of connected components if you only consider cities above a certain value. Component Analysis

37. Consider what happens to the number of connected components if you only consider cities above a certain value. Component Analysis

38. In a biased map, we expect the largest values to be clustered together. Component Analysis

39. In an unbiased map, we expect many random clusters of high heat. Not the single cluster we expect in biased maps. Component Analysis

40. 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. Component Measures

41. To evaluate the measures, we made 4 sets of data maps… Evaluating Bias Measures

42. Random Entity: Uniform

43. Random Entity: Binomial

44. Unbiased Entity

45. Biased Entity

46. Data Set Size 400 biased 128 unbiased 200 uniform 200 binomial Results

47. Discriminating Real Data

48. Improved Map visualization • Sentiment Data Maps. • Animated maps showing temporal changes in popularity. • Improved influence models. • Empirical justifications of models. • Improved bias estimators. Future Work