Determining Common Authorship Among Documents

1. Determining Common Authorship Among Documents Paul Bonamy Mentor: Dr. Paul Kantor

2. Author Identification & Common Authorship • Author Identification: “Who wrote this?” • Mosteller/Wallace, 1964 – The Federalist • 12 disputed papers attributed to Madison • Generally utilizes statistical analysis • Common Authorship: “Do these share an author?” • Does not (necessarily) require statistics/training • Useful for detecting forgeries, etc

3. BMR/BXR • Implements Bayesian Multinomial Regression • Used to perform 1-of-k classification • BMRtrain accepts feature vectors, outputs assignment model • BMRclassify accepts model & vectors, outputs assignments • Can output author probability vectors

4. Bayesian Analysis • Consider two match boxes • Probability of Box 1, given black marble? • H0= We have Box 1, E = We see a black marble

5. Bayesian Analysis in BMR • Bayes’ Theorem Extendable to P(C|F1…FN) • C is a class • F1…FNare features • Effectively applies Bayes’ Theorem to itself

6. BMR/BXR Workflow Data ( Doc Corpus) Test/Train Splitter Training Set Testing Set Feature Extractor Feature Vectors Feature Vectors BMRtrain Model BMRclassify Author Identification Author Probabilities Author Probabilities

7. Corpus Construction • Articles from 2006-07 issues of The Compass Newspaper • 16 Authors • 130 Documents • 300 - 500 Words: 69 • 500+ Words: 61 • Varied Topics • On Friday, November 3, LSSU experienced its first closing of the semester due to inclement weather. The Soo Evening News reported a “number of minor mishaps,” and “slippery-road induced mishaps,” including two crashes near the campus of LSSU. All classes before 10 AM were canceled because of the snow and ice that had accumulated overnight, but many students arrived for classes as usual, unaware of the cancellation. …

8. Feature Extraction • Perl script using Lingua::EN::Tagger • Selects words, part-of-speech (POS), or both (wordPOS) • address/VB • address/NN • Used wordPOS in common authorship study • Returns vector of feature frequencies • 4:9.0 16:5.0 22:4.0 23:2.0 28:5.0 29:1.0 33:4.0 36:9.0 38:1.0 41:3.0 46:13.0 56:2.0 …

9. Author Probability Vectors • Produced by BMR/BXR upon request • Probability doc belongs to each author in the training set • Not normalized (sum not necessarily 1) • 0.17% 0.68% 9.13% 8.90% 2.42% 0.94% 10.55% 0.32% 0.72% 36.95% 0.31% 0.50% 0.48% 22.08% 1.34% 4.52%

10. Computed With Features • Start with feature vectors • Select all distinct pairs of vectors • Compute dot product and Euclidean distance • Sort data • Descending by dot product • Ascending by Euclidean distance

11. Computed With Authors • Start with author probability vectors • Select all distinct pairs of vectors • Compute dot product and Euclidean distance • Sort data • Descending by dot product • Ascending by Euclidean distance

12. What Are We Looking For? • DP and Euclidean distance measure distance • Computed distances between vectors • Sorted from closest to furthest • Docs by same author are close together • Docs by different authors far apart

13. ROC Curve • Shows fractions of not-pairs versus fraction of pairs • Area under curve indicates model accuracy • Higher is better • Euclidean distance of feature vector • This curve: 64.7% of area under curve

14. Can We Improve This?

15. Can We Improve This?

16. Can We Improve This?

17. Can We Improve This?

18. Can We Improve This?

19. Results for Other Data Splits

20. Analyzing Other Corpora • Obtained second corpus • 9377 Documents • 24 Authors • Results similar to those on Compass dataset

21. Open Questions • Are Area Under Curve variations significant? • How does Author ID model accuracy affect same-author accuracy? • A low Author-ID accuracy model did very well • Can we reduce memory/processing requirements?